CN113806522A - Abstract generation method, device, equipment and storage medium - Google Patents
Abstract generation method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN113806522A CN113806522A CN202111098010.XA CN202111098010A CN113806522A CN 113806522 A CN113806522 A CN 113806522A CN 202111098010 A CN202111098010 A CN 202111098010A CN 113806522 A CN113806522 A CN 113806522A
- Authority
- CN
- China
- Prior art keywords
- parameters
- target
- template
- target document
- values
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000004590 computer program Methods 0.000 claims description 13
- 238000000605 extraction Methods 0.000 claims description 8
- 230000004044 response Effects 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 2
- 239000000126 substance Substances 0.000 claims 1
- 239000000284 extract Substances 0.000 abstract description 5
- 238000013473 artificial intelligence Methods 0.000 abstract description 3
- 238000013135 deep learning Methods 0.000 abstract description 3
- 238000003058 natural language processing Methods 0.000 abstract description 2
- 238000004891 communication Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 238000012545 processing Methods 0.000 description 8
- 239000003795 chemical substances by application Substances 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/34—Browsing; Visualisation therefor
- G06F16/345—Summarisation for human users
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/166—Editing, e.g. inserting or deleting
- G06F40/186—Templates
Abstract
The disclosure provides a method, a device, equipment and a storage medium for generating an abstract, and relates to the technical field of artificial intelligence such as the field of natural language processing and deep learning. The specific implementation scheme is as follows: acquiring a target document; determining a plurality of parameters and values of the parameters included in the target document according to the type of the target document and the parameter set matched with the type; determining a target template matched with the target document according to the plurality of parameters; and determining the abstract of the target document according to the values of the parameters and the target template. The implementation mode can automatically extract parameters from the target document, match with a proper template and automatically generate the abstract, thereby improving the generation efficiency of the abstract.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to the field of natural language processing and artificial intelligence technologies such as deep learning, and in particular, to a method, an apparatus, a device, and a storage medium for generating an abstract.
Background
The financial reports are industrial documents commonly used by companies appearing in the market in the financial field, and each financial company can issue the financial reports of seasons, half years and years according to requirements. Financial reports are usually very detailed and abundant, and the financial reports of one company are usually hundreds of pages or more. How to extract key information from financial reports as abstract is important.
Disclosure of Invention
The disclosure provides a summary generation method, a device, equipment and a storage medium.
According to a first aspect, there is provided a digest generation method, including: acquiring a target document; determining a plurality of parameters and values of the parameters included in the target document according to the type of the target document and the parameter set matched with the type; determining a target template matched with the target document according to the plurality of parameters; and determining the abstract of the target document according to the values of the parameters and the target template.
According to a second aspect, there is provided a digest generation apparatus comprising: a document acquisition unit configured to acquire a target document; the parameter determining unit is configured to determine a plurality of parameters included in the target document and values of the plurality of parameters according to the type of the target document and the parameter set matched with the type; a template determination unit configured to determine a target template matching the target document according to a plurality of parameters; and the abstract generating unit is configured to determine the abstract of the target document according to the values of the parameters and the target template.
According to a third aspect, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.
According to a fourth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described in the first aspect.
According to a fifth aspect, a computer program product comprising a computer program which, when executed by a processor, implements the method as described in the first aspect.
According to the technology disclosed by the invention, the parameters can be automatically extracted from the target document, the appropriate template is matched, the abstract is automatically generated, and the generation efficiency of the abstract is improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a digest generation method according to the present disclosure;
FIG. 3 is a schematic diagram of one application scenario of a summary generation method according to the present disclosure;
FIG. 4 is a flow diagram of another embodiment of a digest generation method according to the present disclosure;
FIG. 5 is a schematic block diagram of one embodiment of a digest generation apparatus according to the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing the summary generation method of the embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the summary generation method or summary generation apparatus of the present disclosure may be applied.
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. Various communication client applications, such as a social platform application, a browser application, and the like, may be installed on the terminal devices 101, 102, and 103.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, smart phones, tablet computers, e-book readers, car computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, such as a backend server that performs digest generation of documents provided on the terminal devices 101, 102, 103. The backend server may analyze the target document to obtain the summary, and feed back the summary to the terminal devices 101, 102, and 103.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the summary generation method provided by the embodiment of the present disclosure may be executed by the terminal devices 101, 102, and 103, or may be executed by the server 105. Accordingly, the digest generation apparatus may be provided in the terminal devices 101, 102, and 103, or may be provided in the server 105.
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 continued reference to fig. 2, a flow 200 of one embodiment of a digest generation method according to the present disclosure is shown. The abstract generation method of the embodiment comprises the following steps:
In this embodiment, the execution subject of the digest generation method may acquire the target document in various ways. For example, the executive agent may crawl financial reports, or event statistics documents, published by various companies from the network. Here, the target document may be a document containing a plurality of parameters and parameter values.
The execution subject may determine the type of the target document after obtaining the target document. The types of target documents may include financial reports, event statistics, and the like. The execution subject can analyze the words at the specific position of the target document to determine the type of the target document. For example, the performing agent may analyze the preamble portion of the financial report to determine the type of target document as a financial report. The executing agent may obtain a set of parameters that match the types described above. For example, the set of parameters that match the financial report may include: revenue, total profit, etc. The set of parameters that match the event statistics may include: a three point hit rate, a number of shots taken, etc. The execution subject may find each parameter in the parameter set in the target document, and determine the parameter included in the target document. The executing agent may then also determine values for the parameters from the target document using a deep-learning model, KV (key-value) algorithm.
The execution body may also determine a target template matching the target document according to a plurality of parameters in the target document. Here, the target template may be a template including the plurality of parameters described above. The target template may include a plurality of sentences, each sentence including a plurality of word slots, and the execution subject may fill the plurality of word slots with a plurality of parameters and values of the plurality of parameters.
The execution subject may populate the target template with values for the plurality of parameters to obtain a summary of the target document. Or the execution subject may generate a sentence including values of the plurality of parameters from the sentence included in the target template, resulting in the digest.
With continued reference to fig. 3, a schematic diagram of one application scenario of the summary generation method according to the present disclosure is shown. In the application scenario of fig. 3, the server 301 receives a financial report of a company, and the server 301 first extracts core elements therein and determines values of the respective core elements. And then filling the values of the core elements into a target template to obtain the summary of the financial report.
The abstract generation method provided by the embodiment of the disclosure can automatically extract parameters from the target document, match a proper template, and automatically generate the abstract, thereby improving the generation efficiency of the abstract.
With continued reference to fig. 4, a flow 400 of another embodiment of a digest generation method according to the present disclosure is shown. As shown in fig. 4, the method of the present embodiment may include the following steps:
In this embodiment, the executing agent may extract a plurality of candidate parameters included in the target document using a plurality of parameter extraction algorithms. This is because different parameter extraction algorithms extract different types of parameters with different accuracies. Specifically, the parameter extraction algorithm may include a depth language model, a KV algorithm, a table extraction, and the like. The deep language model is used for extracting fields such as operating profit, net profit, operating income and the like by using a deep learning named entity recognition technology for parameters appearing in paragraphs. For the parameters with relatively fixed and regular format, such as company name, company stock number and financial newspaper release date, KV extraction algorithm is used for extracting corresponding industry parameters. Part of the financial elements appear in the table. For the part of elements, whether the table is extracted or not is judged according to the matching degree of the table header and the element schema, and then the corresponding cell content is extracted.
In this embodiment, in order to unify parameters with the same meaning expressed differently, the above parameters may be normalized, so that the subsequent abstract generation is more accurate.
In this embodiment, the execution subject may obtain the statement set in advance. Each statement in the statement set comprises at least one parameter. The execution body may set a sentence including each of the plurality of parameters as a target sentence. And then combining the target sentences to obtain a target template.
In this embodiment, the execution subject may further determine the target template through step 405:
In this embodiment, the execution subject may obtain the template set in advance. Each template in the set of templates may include a plurality of parameters therein. The execution subject may take a template including the plurality of parameters described above in the template set as a target template.
In this embodiment, if additional parameters are included in the target template in addition to the above parameters, for example, the parameter A, B is included in the target document, and the parameter A, B, C is included in the target template, then the parameter C is an additional parameter. The execution subject may calculate the value of the additional parameter using the values of the plurality of parameters according to the association relationship between the plurality of parameters and the additional parameter. The executing agent may then populate the target template with values for the plurality of parameters and values for the additional parameters, resulting in a summary of the target document.
The abstract generation method provided by the embodiment of the disclosure can determine a proper target template for different target documents, so that the obtained abstract is more accurate.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present disclosure provides an embodiment of a summary generation apparatus, which corresponds to the method embodiment shown in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 5, the digest generation apparatus 500 of the present embodiment includes: a document acquisition unit 501, a parameter determination unit 502, a template determination unit 503, and a digest generation unit 504.
A document acquisition unit 501 configured to acquire a target document.
The parameter determining unit 502 is configured to determine a plurality of parameters included in the target document and values of the plurality of parameters according to the type of the target document and the parameter set matching the type.
A template determination unit 503 configured to determine a target template matching the target document according to the plurality of parameters.
A digest generation unit 504 configured to determine a digest of the target document according to the values of the plurality of parameters and the target template.
In some optional implementations of this embodiment, the template determining unit 503 may be further configured to: determining a target sentence matched with each parameter from a preset sentence set; and combining the target sentences to obtain a target template.
In some optional implementations of this embodiment, the template determining unit 503 may be further configured to: and determining a template containing a plurality of parameters as a target template from a preset template set.
In some optional implementations of this embodiment, the summary generation unit 504 may be further configured to: in response to determining that the target template includes an additional parameter other than the plurality of parameters, calculating values of the additional parameter from the values of the plurality of parameters; and filling the values of the parameters and the values of the additional parameters into the target template to obtain the abstract of the target document.
In some optional implementations of this embodiment, the apparatus 500 may further include a parameter normalization unit, not shown in fig. 5, configured to: a plurality of parameters are normalized.
It should be understood that units 501 to 505 recited in summary generation apparatus 500 correspond to respective steps in the method described with reference to fig. 2. Thus, the operations and features described above for the summary generation method are also applicable to the apparatus 500 and the units included therein, and are not described again here.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to an embodiment of the present disclosure.
Fig. 6 shows a block diagram of an electronic device 600 that performs a digest generation method according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the electronic device 600 includes a processor 601 that may perform various suitable actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a memory 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 can also be stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I/O interface (input/output interface) 605 is also connected to the bus 604.
Various components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a memory 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. The program code described above may be packaged as a computer program product. These program code or computer program products may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor 601, causes the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable storage medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable storage medium may be a machine-readable signal storage medium or a machine-readable storage medium. A machine-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions of the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (15)
1. A summary generation method comprises the following steps:
acquiring a target document;
determining a plurality of parameters and values of the parameters included in the target document according to the type of the target document and the parameter set matched with the type;
determining a target template matched with the target document according to the parameters;
and determining the abstract of the target document according to the values of the parameters and the target template.
2. The method of claim 1, wherein the determining, according to the type of the target document and the set of parameters matching the type, the plurality of parameters and the values of the plurality of parameters included in the target document comprises:
extracting a plurality of candidate parameters included in the target document by using at least one parameter extraction algorithm;
and determining parameters and values of a plurality of parameters included in the target document according to the type of the target document, the parameter set matched with the type and the candidate parameters.
3. The method of claim 1, wherein said determining a target template matching the target document according to the plurality of parameters comprises:
determining a target sentence matched with each parameter from a preset sentence set;
and combining the target sentences to obtain a target template.
4. The method of claim 1, wherein said determining a target template matching the target document according to the plurality of parameters comprises:
and determining a template containing the parameters as a target template from a preset template set.
5. The method of any of claims 1-4, wherein said determining a digest of the target document based on the values of the plurality of parameters and the target template comprises:
in response to determining that the target template includes an additional parameter other than the plurality of parameters, calculating values of the additional parameter from the values of the plurality of parameters;
and filling the values of the parameters and the values of the additional parameters into the target template to obtain the abstract of the target document.
6. The method of any of claims 1-5, wherein the method further comprises:
normalizing the plurality of parameters.
7. A digest generation apparatus comprising:
a document acquisition unit configured to acquire a target document;
a parameter determining unit configured to determine a plurality of parameters included in the target document and values of the plurality of parameters according to the type of the target document and a parameter set matching the type;
a template determination unit configured to determine a target template matching the target document according to the plurality of parameters;
a summary generation unit configured to determine a summary of the target document according to the values of the plurality of parameters and the target template.
8. The apparatus of claim 7, wherein the parameter determination unit is further configured to:
extracting a plurality of candidate parameters included in the target document by using at least one parameter extraction algorithm;
and determining parameters and values of a plurality of parameters included in the target document according to the type of the target document, the parameter set matched with the type and the candidate parameters.
9. The apparatus of claim 7, wherein the template determination unit is further configured to:
determining a target sentence matched with each parameter from a preset sentence set;
and combining the target sentences to obtain a target template.
10. The apparatus of claim 7, wherein the template determination unit is further configured to:
and determining a template containing the parameters as a target template from a preset template set.
11. The apparatus of any of claims 7-10, wherein the summary generation unit is further configured to:
in response to determining that the target template includes an additional parameter other than the plurality of parameters, calculating values of the additional parameter from the values of the plurality of parameters;
and filling the values of the parameters and the values of the additional parameters into the target template to obtain the abstract of the target document.
12. The apparatus according to any one of claims 7-11, wherein the apparatus further comprises a parameter normalization unit configured to:
normalizing the plurality of parameters.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111098010.XA CN113806522A (en) | 2021-09-18 | 2021-09-18 | Abstract generation method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111098010.XA CN113806522A (en) | 2021-09-18 | 2021-09-18 | Abstract generation method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113806522A true CN113806522A (en) | 2021-12-17 |
Family
ID=78896068
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111098010.XA Pending CN113806522A (en) | 2021-09-18 | 2021-09-18 | Abstract generation method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113806522A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115309888A (en) * | 2022-08-26 | 2022-11-08 | 百度在线网络技术（北京）有限公司 | Method and device for generating chart abstract and method and device for training generated model |
| CN117453964A (en) * | 2023-10-08 | 2024-01-26 | 北京融和云链科技有限公司 | Template-based data structure acquisition and target template generation method and device |
| CN117453964B (en) * | 2023-10-08 | 2024-04-30 | 北京融和云链科技有限公司 | Template-based data structure acquisition and target template generation method and device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102279846A (en) * | 2010-06-10 | 2011-12-14 | 英业达股份有限公司 | Article assisting writing system and method thereof |
| CN104636431A (en) * | 2014-12-31 | 2015-05-20 | 南京新模式软件集成有限公司 | Automatic extraction and optimizing method for document abstracts of different fields |
| CN110069623A (en) * | 2017-12-06 | 2019-07-30 | 腾讯科技（深圳）有限公司 | Summary texts generation method, device, storage medium and computer equipment |
| CN111428041A (en) * | 2019-01-09 | 2020-07-17 | 阿里巴巴集团控股有限公司 | Case abstract generation method, device, system and storage medium |
| CN111552800A (en) * | 2020-03-31 | 2020-08-18 | 深圳壹账通智能科技有限公司 | Abstract generation method and device, electronic equipment and medium |
| CN113361240A (en) * | 2021-06-23 | 2021-09-07 | 北京百度网讯科技有限公司 | Method, device, equipment and readable storage medium for generating target article |
-
2021
- 2021-09-18 CN CN202111098010.XA patent/CN113806522A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102279846A (en) * | 2010-06-10 | 2011-12-14 | 英业达股份有限公司 | Article assisting writing system and method thereof |
| CN104636431A (en) * | 2014-12-31 | 2015-05-20 | 南京新模式软件集成有限公司 | Automatic extraction and optimizing method for document abstracts of different fields |
| CN110069623A (en) * | 2017-12-06 | 2019-07-30 | 腾讯科技（深圳）有限公司 | Summary texts generation method, device, storage medium and computer equipment |
| CN111428041A (en) * | 2019-01-09 | 2020-07-17 | 阿里巴巴集团控股有限公司 | Case abstract generation method, device, system and storage medium |
| CN111552800A (en) * | 2020-03-31 | 2020-08-18 | 深圳壹账通智能科技有限公司 | Abstract generation method and device, electronic equipment and medium |
| CN113361240A (en) * | 2021-06-23 | 2021-09-07 | 北京百度网讯科技有限公司 | Method, device, equipment and readable storage medium for generating target article |
Non-Patent Citations (1)
| Title |
|---|
| 陈燕 等: "数据挖掘与聚类分析", 30 November 2012, 大连海事大学出版社, pages: 333 - 334 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115309888A (en) * | 2022-08-26 | 2022-11-08 | 百度在线网络技术（北京）有限公司 | Method and device for generating chart abstract and method and device for training generated model |
| CN117453964A (en) * | 2023-10-08 | 2024-01-26 | 北京融和云链科技有限公司 | Template-based data structure acquisition and target template generation method and device |
| CN117453964B (en) * | 2023-10-08 | 2024-04-30 | 北京融和云链科技有限公司 | Template-based data structure acquisition and target template generation method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20230004721A1 (en) | Method for training semantic representation model, device and storage medium | |
| CN113836314B (en) | Knowledge graph construction method, device, equipment and storage medium | |
| US20230134615A1 (en) | Method of processing task, electronic device, and storage medium | |
| CN111143505A (en) | Document processing method, device, medium and electronic equipment | |
| CN113657395A (en) | Text recognition method, and training method and device of visual feature extraction model | |
| US20230005283A1 (en) | Information extraction method and apparatus, electronic device and readable storage medium | |
| CN111651552A (en) | Structured information determination method and device and electronic equipment | |
| EP3920074A2 (en) | Method for industry text increment, related apparatus, and computer program product | |
| CN112926298A (en) | News content identification method, related device and computer program product | |
| CN113806522A (en) | Abstract generation method, device, equipment and storage medium | |
| CN114461665B (en) | Method, apparatus and computer program product for generating a statement transformation model | |
| US20210342379A1 (en) | Method and device for processing sentence, and storage medium | |
| CN113377924A (en) | Data processing method, device, equipment and storage medium | |
| CN114238611A (en) | Method, apparatus, device and storage medium for outputting information | |
| CN117272970B (en) | Document generation method, device, equipment and storage medium | |
| CN114492409B (en) | Method and device for evaluating file content, electronic equipment and program product | |
| CN114491040B (en) | Information mining method and device | |
| CN112989797B (en) | Model training and text expansion methods, devices, equipment and storage medium | |
| CN115965018B (en) | Training method of information generation model, information generation method and device | |
| EP4131022A1 (en) | Method and apparatus of determining location information, electronic device, storage medium, and program product | |
| CN114281981B (en) | News brief report generation method and device and electronic equipment | |
| US20220237388A1 (en) | Method and apparatus for generating table description text, device and storage medium | |
| US20220391602A1 (en) | Method of federated learning, electronic device, and storage medium | |
| CN113342990A (en) | Knowledge graph construction method and device | |
| CN116069914A (en) | Training data generation method, model training method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |