CN110866110A - Conference summary generation method, device, equipment and medium based on artificial intelligence - Google Patents
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Abstract
The embodiment of the application discloses a conference summary generation method, a device, equipment and a medium based on artificial intelligence, and relates to the technical field of communication information recording. The method comprises the following steps: acquiring an initial conference record of a target conference; identifying non-text information and first text information in the initial conference record, converting the non-text information into second text information, and recording the first text information and the second text information as text information to be processed; calling a preset fuzzy clustering model, performing fuzzy clustering processing on the text information to be processed through the fuzzy clustering model, and extracting conference content from the text information to be processed; and generating a conference summary according to the conference content. According to the method, the contents of the conference records are quickly sorted in a fuzzy clustering analysis mode, so that the waste of human resources can be reduced, the sorting difficulty is reduced, the omission of the contents can be effectively avoided when the conference summary is output, the accuracy of the conference summary is improved, and the efficiency of sorting the conference contents is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of communication information recording, in particular to a conference summary generation method, a device, equipment and a medium based on artificial intelligence.
Background
The conference is a common social phenomenon, which means that people carry out information exchange or activities of gathering and discussing around a common theme with the same or different purposes. The conference recording refers to recording the organization condition and the specific content of the conference by recording personnel or participants in the conference. The conference era is a narrative and introductory file processed and arranged on the basis of conference records.
In daily work, according to communication needs, a subject is surrounded in a conference form, so that a common problem is solved or the communication is carried out together for different purposes. For example, various types of conferences such as a communication conference, a requirement review conference, an information announcement, and the like. In the conference process, whether the conference is in a network meeting or an actual meeting, the conference participants or the recorders record the conference contents, or the conference contents are stored and identified through a voice device, or the conference participants or the recorders record the conference contents on own notebook through handwriting characters. After the meeting is finished, a large amount of time is usually spent for arranging meeting records, information is subjected to structured processing, and then a meeting summary is arranged.
However, the mode of generating the conference summary by integrating the conference contents recorded by each participant through special personnel is low in efficiency, poor in convenience, easy to cause waste of human resources and high in difficulty of information summarization. For a conference with multiple topics, the same topic may be discussed alternately, the recording positions of the conference are crossed, and it is difficult to classify the same topic information into the same physical position for storage management, so that the user may miss information during subsequent viewing.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present application is to provide a conference summary generation method, device, equipment and medium based on artificial intelligence, which effectively categorize conference information through fuzzy clustering processing, reduce the categorization difficulty of information and improve the generation efficiency.
In order to solve the above technical problem, the conference summary generation method based on artificial intelligence according to the embodiment of the present application adopts the following technical scheme:
a conference summary generation method based on artificial intelligence comprises the following steps:
acquiring an initial conference record of a target conference;
preprocessing the initial conference record to identify non-text information and first text information in the initial conference record, converting the non-text information into second text information, and recording the first text information and the second text information as text information to be processed;
calling a preset fuzzy clustering model, performing fuzzy clustering processing on the text information to be processed through the fuzzy clustering model, and extracting conference content from the text information to be processed;
and generating a conference summary according to the conference content.
The conference summary generation method based on artificial intelligence can be used for rapidly arranging the contents of the conference records in a fuzzy clustering analysis mode, waste of human resources can be reduced, the difficulty in induction is reduced, omission of the contents can be effectively avoided when the conference summary is output, accuracy of the conference summary is improved, and efficiency of arranging the conference contents is improved.
Further, the method for generating a conference summary based on artificial intelligence, wherein the step of converting the non-text information into the second text information comprises:
analyzing the type of non-text information in the initial conference record;
if the image type exists, extracting an image type record in the initial conference record, and performing ocr processing on the image type record to acquire second text information represented by the image type record;
if the voice type exists, extracting the voice type record in the initial conference record, and executing voice recognition on the voice type record to acquire second text information represented by the voice type record;
and if the video type exists, extracting the video type record in the initial conference record, further extracting the video image record and the video audio record in the video type record, and performing ocr processing on the video image record and performing voice recognition on the video audio record to acquire second text information represented by the video type record.
Further, the method for generating a conference summary based on artificial intelligence, wherein the step of performing fuzzy clustering processing on the text information to be processed through the fuzzy clustering model and extracting conference content from the text information to be processed includes:
vectorizing the text information to be processed to respectively convert each sentence into an n-dimensional vector;
acquiring a set cluster value for fuzzy clustering and a central point of each cluster, and calculating the similarity between an n-dimensional vector corresponding to each sentence in the text information to be processed and each central point through the fuzzy clustering model;
classifying the text information to be processed based on the cluster value and the similarity so as to divide the text information to be processed into conference contents with the category number corresponding to the cluster value.
Further, the method for generating a conference summary based on artificial intelligence includes the steps of vectorizing the text information to be processed to convert each sentence into an n-dimensional vector, respectively:
sentence breaking is carried out on the text information to be processed, and word segmentation is carried out on each sentence based on the text information to be processed after sentence breaking;
acquiring n basic texts contained in the text information to be processed based on the word segmentation result, and generating n dimensions of the text information to be processed by taking each basic text as a dimension;
and counting the word frequency of each sentence in each dimension, and respectively converting each sentence in the text information to be processed into an n-dimensional vector according to the word frequency.
Further, after the step of dividing the text information to be processed into the conference content with the category number corresponding to the cluster value, the method for generating the conference summary based on the artificial intelligence further includes the steps of:
and acquiring a deduplication threshold, calculating the similarity between sentences in each category of the conference content, and performing deduplication processing on the sentences of which the similarity is greater than the deduplication threshold.
Further, after the step of generating the conference summary according to the conference content, the method for generating the conference summary based on the artificial intelligence further includes the steps of:
sending the generated conference summary to the participants of the target conference;
receiving an editing request of a participant to the conference summary;
and opening an editing function for the conference summary to the participants in response to the editing request.
Further, after the step of generating the conference summary according to the conference content, the method for generating the conference summary based on the artificial intelligence further includes the steps of:
receiving a reading request of a target user for the conference summary, wherein the reading request comprises a target time period and/or a target topic which is expected to be requested;
analyzing the user permission of the target user, and judging whether the target user has the permission to read the conference summary;
and after the target user is confirmed to have the right of reading the conference summary, extracting target conference summary content from the conference summary based on the target time period and/or the target topic, and sending the target conference summary content to the target user in response to the reading request.
In order to solve the above technical problem, an embodiment of the present application further provides a conference summary generation apparatus based on artificial intelligence, which adopts the following technical scheme:
an artificial intelligence based conference summary generation apparatus comprising:
the recording acquisition module is used for acquiring an initial conference record of the target conference;
the information conversion module is used for preprocessing the initial conference record to identify non-text information and first text information in the initial conference record, converting the non-text information into second text information, and recording the first text information and the second text information as text information to be processed;
the data processing module is used for calling a preset fuzzy clustering model, carrying out fuzzy clustering processing on the text information to be processed through the fuzzy clustering model and extracting conference content from the text information to be processed;
and the summary generation module is used for generating a conference summary according to the conference content.
The embodiment of the application the conference summary generation device based on artificial intelligence, the contents of the conference records are quickly arranged in a fuzzy clustering analysis mode, waste of human resources can be reduced, the difficulty in induction is reduced, omission of the contents can be effectively avoided when the conference summary is output, accuracy of the conference summary is improved, and efficiency of arranging the conference contents is improved.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
a computer device comprising a memory in which a computer program is stored and a processor, the processor implementing the steps of the artificial intelligence based conference summary generation method according to any one of the preceding claims when executing the computer program.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the artificial intelligence based conference summary generation method according to any one of the preceding claims.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
the embodiment of the application discloses a conference summary generation method, a device, equipment and a medium based on artificial intelligence, wherein the conference summary generation method based on artificial intelligence obtains an initial conference record of a target conference; preprocessing the initial conference record to identify non-text information and first text information in the initial conference record, converting the non-text information into second text information, and recording the first text information and the second text information as text information to be processed; then calling a preset fuzzy clustering model, performing fuzzy clustering processing on the text information to be processed through the fuzzy clustering model, and extracting conference content from the text information to be processed; and finally generating a conference summary according to the conference content. According to the method, the contents of the conference records are quickly sorted in a fuzzy clustering analysis mode, so that the waste of human resources can be reduced, the sorting difficulty is reduced, the omission of the contents can be effectively avoided when the conference summary is output, the accuracy of the conference summary is improved, and the efficiency of sorting the conference contents is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a diagram of an exemplary system architecture to which embodiments of the present application may be applied;
FIG. 2 is a flowchart of an embodiment of a method for generating a meeting summary based on artificial intelligence in an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an embodiment of the artificial intelligence based conference summary generation apparatus in the embodiment of the present application;
fig. 4 is a schematic structural diagram of an embodiment of a computer device in an embodiment of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It is noted that the terms "comprises," "comprising," and "having" and any variations thereof in the description and claims of this application and the drawings described above are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. In the claims, the description and the drawings of the specification of the present application, relational terms such as "first" and "second", and the like, may be used solely to distinguish one entity/action/object from another entity/action/object without necessarily requiring or implying any actual such relationship or order between such entities/actions/objects.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the relevant drawings in the embodiments of the present application.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the conference summary generation method based on artificial intelligence provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the conference summary generation apparatus based on artificial intelligence is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continuing reference to FIG. 2, a flowchart of one embodiment of the artificial intelligence based conference summary generation method described in embodiments of the present application is shown. The conference summary generation method based on artificial intelligence comprises the following steps:
step 201: and acquiring an initial conference record of the target conference.
The conference recording is that recording personnel or conference participants record organization conditions and specific contents of a conference, and recording forms include a writing recording mode, a sound recording mode, an image recording mode and the like.
Generally, for voice files and video files recorded by audio recording and video recording, the files are required to be converted into text files, and the organization condition and specific content of the conference are recorded in the form of a transaction document. Further, the conference records are classified according to the conference property, and the conference records include office conference records, special conference records, joint (coordinated) conference records, and seating conference records.
In this embodiment of the present application, an electronic device (for example, the server/terminal device shown in fig. 1) on which the artificial intelligence based conference summary generation method operates may receive an initial conference record of a target conference through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
Step 202: preprocessing the initial conference record to identify non-text information and first text information in the initial conference record, converting the non-text information into second text information, and recording the first text information and the second text information as text information to be processed.
The structured information is data information which can be digitalized and can be conveniently managed through computer and database technologies. Information that cannot be fully digitized is referred to as unstructured information, such as document files, pictures, drawing data, microfilms, and the like. The process of generating the conference summary from the initial conference record is essentially understood as a process of converting unstructured information into structured information and normalizing the conference information so as to arrange the conference information.
In an embodiment of the present application, the initial conference record includes: one or more of text recording, image recording, voice recording, and video recording.
In a specific implementation manner of the embodiment of the present application, the step of converting the non-text information into the second text information in step 202 includes:
analyzing the type of non-text information in the initial conference record;
if the image type exists, extracting an image type record in the initial conference record, and performing ocr processing on the image type record to acquire second text information represented by the image type record;
if the voice type exists, extracting the voice type record in the initial conference record, and executing voice recognition on the voice type record to acquire second text information represented by the voice type record;
and if the video type exists, extracting the video type record in the initial conference record, further extracting the video image record and the video audio record in the video type record, and performing ocr processing on the video image record and performing voice recognition on the video audio record to acquire second text information represented by the video type record.
The image type records, the voice type records and/or the video type records contained in the conference records are converted into text records for structural processing, and then the text records can be conveniently further sorted.
Specifically, the type of the non-text information in the initial conference record includes an image type, such as a conference record, if the conference record is an image of a handwriting record, it needs to be processed by OCR (Optical Character Recognition) to be converted into an electronic text. For types of non-text information including speech type, the speech type record is converted into corresponding electronic text by speech recognition. If the type of the non-text information comprises a video type, the recording form of the video recording can comprise two modes of recording the conference information through audio and recording the conference information through image streams, so that different processing modes are required to be respectively adopted for the two different recording forms; where the video type recording includes both video image recording and video audio recording, performing ocr processing on the video image recording and performing speech recognition on the video audio recording may be converted to responsive electronic text.
Further, if the text information to be processed obtained from the initial conference record includes more than one voice, the text information to be processed also needs to be subjected to language conversion so as to unify the languages of the text information to be processed.
Step 203: and calling a preset fuzzy clustering model, performing fuzzy clustering processing on the text information to be processed through the fuzzy clustering model, and extracting conference content from the text information to be processed.
The fuzzy clustering processing aims at classifying the text information to be processed, summarizing and arranging meeting contents through classification, performing structural processing on the information, classifying the meeting contents, enabling meeting records to be clearer and clearer, and performing specific output aiming at classification types when meeting minutes are output, for example, relevant meeting minutes aiming at a certain topic through classifying the meeting records.
In some embodiments of the present application, in step 203, performing fuzzy clustering processing on the to-be-processed text information through the fuzzy clustering model, and the step of extracting meeting content from the to-be-processed text information includes:
vectorizing the text information to be processed to respectively convert each sentence into an n-dimensional vector;
acquiring a set cluster value for fuzzy clustering and a central point of each cluster, and calculating the similarity between an n-dimensional vector corresponding to each sentence in the text information to be processed and each central point through the fuzzy clustering model;
classifying the text information to be processed based on the cluster value and the similarity so as to divide the text information to be processed into conference contents with the category number corresponding to the cluster value.
In the embodiment of the present application, the cluster value substantially refers to the number of the clustered categories, that is, the number of the classified categories after the text information to be processed is classified. Setting the number of clusters (i.e. cluster value) as k, where a central point refers to a cluster center that divides the text information to be processed into each of k categories, and is used to determine whether to divide the corresponding text information into the category corresponding to the central point, where the number of central points is the same as the value of the cluster value.
The cluster value and the center point may be set by manual input or may be automatically set by the fuzzy clustering model. The cluster value can be set according to the number of topics of the conference, the conference content and other contents. Specifically, the fuzzy clustering model may apply some calculation algorithms to calculate the clustered cluster values, so as to obtain the cluster values corresponding to the optimal clustering scheme. The central point can be obtained by selecting k sentences from the text information to be processed by an algorithm program applied by a fuzzy clustering model as the central point under each category, or by manually selecting one sentence as the central point according to each topic.
During classification, the fuzzy clustering model calculates the similarity between the n-dimensional vector corresponding to each sentence and each central point, and after counting the calculation results, judges which central point the similarity between each sentence and which is the highest so as to classify each sentence into the category corresponding to the central point with the highest similarity, thereby classifying the text information to be processed. The number of categories is the same as the number of said centre points, i.e. the cluster value.
In a further implementation manner of the embodiment of the present application, the vectorizing the text information to be processed to convert each sentence into an n-dimensional vector includes:
sentence breaking is carried out on the text information to be processed, and word segmentation is carried out on each sentence based on the text information to be processed after sentence breaking;
acquiring n basic texts contained in the text information to be processed based on the word segmentation result, and generating n dimensions of the text information to be processed by taking each basic text as a dimension;
and counting the word frequency of each sentence in each dimension, and respectively converting each sentence in the text information to be processed into an n-dimensional vector according to the word frequency.
The text information to be processed is divided into a plurality of sentences by sentence breaking processing, then each sentence is divided into words, the dimensionality of the sentence is listed, and then the sentence is vectorized according to the word frequency.
The example of the sentence a and the sentence B obtained after the sentence break is performed on the text information to be processed is as follows:
sentence a: and a business target builds an intelligent financial operation platform.
Sentence B: the business goal is to build an intelligent financial operations platform.
The word segmentation result is as follows:
sentence a: business/goal, build/one/intelligent/financial/operations/platform.
Sentence B: business/goal/yes/construction/intelligence/finance/operation/platform.
The dimensions of the text information to be processed listed are 9, and the specific contents of the basic text corresponding to the 9 dimensions are as follows:
business, goal, construction, one, intelligence, finance, operations, platform, is.
The word frequencies of the sentence A and the sentence B under the above 9 dimensions are counted:
sentence a: business 1, goal 1, build 1, one 1, intelligence 1, finance 1, operations 1, platform 1, is 0.
Sentence B: business 1, goal 1, build 1, one 0, intelligence 1, finance 1, operations 1, platform 1, is 1.
The 9-dimensional vector thus converted from sentences a and B is represented as follows:
sentence a: [1,1,1,1,1,1,1,1,0]
Sentence B: [1,1,1,0,1,1,1,1,1]
And a process of calculating similarity, namely a process of calculating similarity between the n-dimensional vector corresponding to the sentence and the n-dimensional vector corresponding to the central point.
In a further implementation manner of this embodiment, after the step of dividing the text information to be processed into the conference content whose category number corresponds to the cluster value, the artificial intelligence based conference summary generation method further includes the steps of: and acquiring a deduplication threshold, calculating the similarity between sentences in each category of the conference content, and performing deduplication processing on the sentences of which the similarity is greater than the deduplication threshold.
Similar information is inevitable to appear in the conference record, especially when the conference record is recorded by multiple persons, the same kind of information is easy to be redundant, because the steps just aggregate and classify the same kind of information, the data is easy to generate redundancy, but for the conference summary, the redundant information is not required to be reserved, and therefore the classified conference content is required to be deduplicated to remove repeated or excessively similar parts in the conference content.
Specifically, when duplicate content is detected in the meeting summary, the meeting record with the earliest or latest recording time can be reserved, and other duplicate content can be deleted. For contents that are too similar, a deduplication threshold may be preset, for example, when the deduplication threshold is set to 90%, if the similarity between two sentences reaches above 90%, the sentence with the most number of words may be selected to retain the sentence with the most complete expression.
Step 204: and generating a conference summary according to the conference content.
The conference summary refers to a conference report generated for a certain conference, the initial conference records of the conference are rapidly sorted through the processing of steps 201 to 203,
in a specific implementation manner of the embodiment of the present application, after step 204, the method for generating a conference summary based on artificial intelligence further includes the steps of:
sending the generated conference summary to the participants of the target conference;
receiving an editing request of a participant to the conference summary;
and opening an editing function for the conference summary to the participants in response to the editing request.
After the meeting summary is generated, sometimes the content of the meeting summary needs to be further checked and corrected, at this time, the generated meeting summary can be put to part or all of the participants of the target meeting, if the participants find out the place needing to be corrected after checking the content of the meeting summary, an editing request can be sent to the current server, and after the server receives the editing request of the participants to the meeting summary, the server opens the editing function of the meeting summary to the participants, so that the relevant participants finish correcting the content of the meeting, and the accuracy of the content of the meeting summary is improved.
In a specific implementation manner of the embodiment of the present application, after step 204, the method for generating a conference summary based on artificial intelligence further includes the steps of:
receiving a reading request of a target user for the conference summary, wherein the reading request comprises a target time period and/or a target topic which is expected to be requested;
analyzing the user permission of the target user, and judging whether the target user has the permission to read the conference summary;
and after the target user is confirmed to have the right of reading the conference summary, extracting target conference summary content from the conference summary based on the target time period and/or the target topic, and sending the target conference summary content to the target user in response to the reading request.
If the content of the conference summary is too much, part of the target users do not need to read all the conference summaries, but only want to read the conference summary for a certain time period or a certain topic. The target user can edit a reading request including a target time period and/or a target topic which is expected to be requested for the conference summary, and then the reading request is sent to the server, after the server receives the reading request, whether the target user has corresponding authority is further confirmed, if the server confirms that the target user has the corresponding authority, responsive contents are screened and extracted from the conference summary according to the target time period and/or the target topic and then sent to the target user, and therefore reading efficiency of the target user can be improved, and user experience is improved.
The conference summary generation method based on artificial intelligence can be used for rapidly arranging the contents of the conference records in a fuzzy clustering analysis mode, waste of human resources can be reduced, the difficulty in induction is reduced, omission of the contents can be effectively avoided when the conference summary is output, accuracy of the conference summary is improved, and efficiency of arranging the conference contents is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 3, fig. 3 is a schematic structural diagram of an embodiment of an artificial intelligence based conference summary generation apparatus according to the embodiment of the present application. As an implementation of the method shown in fig. 2, the present application provides an embodiment of an artificial intelligence based conference summary generation apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 3, the artificial intelligence based conference summary generation apparatus according to this embodiment includes:
a record acquisition module 301; for obtaining an initial meeting record of the target meeting.
An information conversion module 302; the conference processing device is used for preprocessing the initial conference record to identify non-text information and first text information in the initial conference record, converting the non-text information into second text information, and recording the first text information and the second text information as text information to be processed.
A data processing module 303; the system comprises a fuzzy clustering module and a conference content extraction module, wherein the fuzzy clustering module is used for calling a preset fuzzy clustering model, carrying out fuzzy clustering processing on the text information to be processed through the fuzzy clustering model, and extracting the conference content from the text information to be processed.
A summary generation module 304; and generating a conference summary according to the conference content.
In a specific implementation manner of the embodiment of the present application, the information conversion module 302 further includes: and the information type identification submodule. The information type identification submodule is used for analyzing the type of the non-text information in the initial conference record; if the image type exists, extracting an image type record in the initial conference record, and performing ocr processing on the image type record to acquire second text information represented by the image type record; if the voice type exists, extracting the voice type record in the initial conference record, and executing voice recognition on the voice type record to acquire second text information represented by the voice type record; and if the video type exists, extracting the video type record in the initial conference record, further extracting the video image record and the video audio record in the video type record, and performing ocr processing on the video image record and performing voice recognition on the video audio record to acquire second text information represented by the video type record.
In some embodiments of the present application, the data processing module 303 further includes: and a text classification submodule. The text classification submodule is used for vectorizing the text information to be processed so as to respectively convert each sentence into an n-dimensional vector; acquiring a set cluster value for fuzzy clustering and a central point of each cluster, and calculating the similarity between an n-dimensional vector corresponding to each sentence in the text information to be processed and each central point through the fuzzy clustering model; classifying the text information to be processed based on the cluster value and the similarity so as to divide the text information to be processed into conference contents with the category number corresponding to the cluster value.
In a further implementation manner of the embodiment of the present application, the text classification sub-module is configured to perform sentence segmentation on the to-be-processed text information, and perform word segmentation on each sentence based on the to-be-processed text information after the sentence segmentation; acquiring n basic texts contained in the text information to be processed based on the word segmentation result, and generating n dimensions of the text information to be processed by taking each basic text as a dimension; and counting the word frequency of each sentence in each dimension, and respectively converting each sentence in the text information to be processed into an n-dimensional vector according to the word frequency.
In a further implementation manner of the embodiment of the present application, the apparatus for generating a conference summary based on artificial intelligence further includes: and a deduplication module. After the text classification submodule divides the text information to be processed into the conference content with the category number corresponding to the cluster value, the duplication elimination module is used for obtaining a duplication elimination threshold, calculating the similarity between sentences in each category of the conference content, and carrying out duplication elimination processing on the sentences with the similarity larger than the duplication elimination threshold.
In a specific implementation manner of the embodiment of the present application, the apparatus for generating a conference summary based on artificial intelligence further includes: and a summary error correction module. After the summary generation module 304 generates a conference summary according to the conference content, the summary correction module is configured to send the generated conference summary to the attendees of the target conference; receiving an editing request of a participant to the conference summary; and opening an editing function for the conference summary to the participants in response to the editing request.
In a specific implementation manner of the embodiment of the present application, the apparatus for generating a conference summary based on artificial intelligence further includes: a summary sending module. After the summary generation module 304 generates a conference summary according to the conference content, the summary transmission module is configured to receive a reading request of a target user for the conference summary, where the reading request includes a target time period and/or a target topic that the user wants to request;
analyzing the user permission of the target user, and judging whether the target user has the permission to read the conference summary;
and after the target user is confirmed to have the right of reading the conference summary, extracting target conference summary content from the conference summary based on the target time period and/or the target topic, and sending the target conference summary content to the target user in response to the reading request.
The embodiment of the application the conference summary generation device based on artificial intelligence, the contents of the conference records are quickly arranged in a fuzzy clustering analysis mode, waste of human resources can be reduced, the difficulty in induction is reduced, omission of the contents can be effectively avoided when the conference summary is output, accuracy of the conference summary is improved, and efficiency of arranging the conference contents is improved.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 61-63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable gate array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash Card (FlashCard), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various types of application software, such as program codes of an artificial intelligence-based conference summary generation method. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute the program code stored in the memory 61 or process data, for example, execute the program code of the artificial intelligence based conference summary generation method.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
The present application further provides another embodiment, which provides a computer-readable storage medium storing an artificial intelligence based conference summary generation program executable by at least one processor to cause the at least one processor to perform the steps of the artificial intelligence based conference summary generation method as described above.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
In the above embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The modules or components may or may not be physically separate, and the components shown as modules or components may or may not be physical modules, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules or components can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The present application is not limited to the above-mentioned embodiments, the above-mentioned embodiments are preferred embodiments of the present application, and the present application is only used for illustrating the present application and not for limiting the scope of the present application, it should be noted that, for a person skilled in the art, it is still possible to make several improvements and modifications to the technical solutions described in the foregoing embodiments or to make equivalent substitutions for some technical features without departing from the principle of the present application. All equivalent structures made by using the contents of the specification and the drawings of the present application can be directly or indirectly applied to other related technical fields, and the same should be considered to be included in the protection scope of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All other embodiments that can be obtained by a person skilled in the art based on the embodiments in this application without any creative effort and all equivalent structures made by using the contents of the specification and the drawings of this application can be directly or indirectly applied to other related technical fields and are within the scope of protection of the present application.
Claims (10)
1. A conference summary generation method based on artificial intelligence is characterized by comprising the following steps:
acquiring an initial conference record of a target conference;
preprocessing the initial conference record to identify non-text information and first text information in the initial conference record, converting the non-text information into second text information, and recording the first text information and the second text information as text information to be processed;
calling a preset fuzzy clustering model, performing fuzzy clustering processing on the text information to be processed through the fuzzy clustering model, and extracting conference content from the text information to be processed;
and generating a conference summary according to the conference content.
2. The artificial intelligence based conference summary generation method of claim 1, wherein the step of converting the non-textual information into second textual information comprises:
analyzing the type of non-text information in the initial conference record;
if the image type exists, extracting an image type record in the initial conference record, and performing ocr processing on the image type record to acquire second text information represented by the image type record;
if the voice type exists, extracting the voice type record in the initial conference record, and executing voice recognition on the voice type record to acquire second text information represented by the voice type record;
and if the video type exists, extracting the video type record in the initial conference record, further extracting the video image record and the video audio record in the video type record, and performing ocr processing on the video image record and performing voice recognition on the video audio record to acquire second text information represented by the video type record.
3. The artificial intelligence based conference summary generation method according to claim 1, wherein the step of performing fuzzy clustering processing on the text information to be processed through the fuzzy clustering model and extracting conference contents from the text information to be processed comprises:
vectorizing the text information to be processed to respectively convert each sentence into an n-dimensional vector;
acquiring a set cluster value for fuzzy clustering and a central point of each cluster, and calculating the similarity between an n-dimensional vector corresponding to each sentence in the text information to be processed and each central point through the fuzzy clustering model;
classifying the text information to be processed based on the cluster value and the similarity so as to divide the text information to be processed into conference contents with the category number corresponding to the cluster value.
4. The artificial intelligence based conference summary generation method according to claim 3, wherein the step of vectorizing the text information to be processed to convert each sentence into an n-dimensional vector comprises:
sentence breaking is carried out on the text information to be processed, and word segmentation is carried out on each sentence based on the text information to be processed after sentence breaking;
acquiring n basic texts contained in the text information to be processed based on the word segmentation result, and generating n dimensions of the text information to be processed by taking each basic text as a dimension;
and counting the word frequency of each sentence in each dimension, and respectively converting each sentence in the text information to be processed into an n-dimensional vector according to the word frequency.
5. The artificial intelligence based conference summary generation method according to claim 3, wherein after the step of dividing the text information to be processed into the conference contents whose category number corresponds to the cluster value, the method further comprises the steps of:
and acquiring a deduplication threshold, calculating the similarity between sentences in each category of the conference content, and performing deduplication processing on the sentences of which the similarity is greater than the deduplication threshold.
6. The artificial intelligence based conference summary generation method according to claim 1, wherein after the step of generating a conference summary from the conference content, the method further comprises the steps of:
sending the generated conference summary to the participants of the target conference;
receiving an editing request of a participant to the conference summary;
and opening an editing function for the conference summary to the participants in response to the editing request.
7. The artificial intelligence based conference summary generation method according to claim 1, wherein after the step of generating a conference summary from the conference content, the method further comprises the steps of:
receiving a reading request of a target user for the conference summary, wherein the reading request comprises a target time period and/or a target topic which is expected to be requested;
analyzing the user permission of the target user, and judging whether the target user has the permission to read the conference summary;
and after the target user is confirmed to have the right of reading the conference summary, extracting target conference summary content from the conference summary based on the target time period and/or the target topic, and sending the target conference summary content to the target user in response to the reading request.
8. An artificial intelligence based conference summary generation apparatus, comprising:
the recording acquisition module is used for acquiring an initial conference record of the target conference;
the information conversion module is used for preprocessing the initial conference record to identify non-text information and first text information in the initial conference record, converting the non-text information into second text information, and recording the first text information and the second text information as text information to be processed;
the data processing module is used for calling a preset fuzzy clustering model, carrying out fuzzy clustering processing on the text information to be processed through the fuzzy clustering model and extracting conference content from the text information to be processed;
and the summary generation module is used for generating a conference summary according to the conference content.
9. A computer device comprising a memory having stored therein a computer program and a processor which when executed implements the steps of the artificial intelligence based conference summary generation method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the artificial intelligence based conference summary generation method according to any one of claims 1-7.
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| CN113642294A (en) * | 2021-08-13 | 2021-11-12 | 百度在线网络技术（北京）有限公司 | Method, apparatus, device, medium and product for generating a conference summary |
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| CN111523126A (en) * | 2020-03-19 | 2020-08-11 | 浙江省北大信息技术高等研究院 | Conference recording authority management method, device and storage medium |
| CN111723205A (en) * | 2020-06-18 | 2020-09-29 | 中国银行股份有限公司 | Conference summary processing method and device and conference summary processing equipment |
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| CN112836476A (en) * | 2021-02-04 | 2021-05-25 | 北京字跳网络技术有限公司 | Summary generation method, device, equipment and medium |
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| CN113642294A (en) * | 2021-08-13 | 2021-11-12 | 百度在线网络技术（北京）有限公司 | Method, apparatus, device, medium and product for generating a conference summary |
| CN113642294B (en) * | 2021-08-13 | 2023-08-04 | 百度在线网络技术（北京）有限公司 | Method, apparatus, device, medium and product for generating meeting summary |
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| US11837219B2 (en) | 2021-11-18 | 2023-12-05 | International Business Machines Corporation | Creation of a minute from a record of a teleconference |
| CN115658956A (en) * | 2022-11-02 | 2023-01-31 | 无锡东方健康科技有限公司 | Hot topic mining method and system based on conference audio data |
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