CN115687628A - News quality judging method, system, computer equipment and storage medium - Google Patents
News quality judging method, system, computer equipment and storage medium Download PDFInfo
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Abstract
The application discloses a news quality evaluation method, a system, a computer device and a storage medium, comprising: acquiring a plurality of texts to be evaluated from news contents to be evaluated; vectorizing the texts to be judged to obtain text vectors; inputting the text vector into a quality evaluation model, and outputting a multi-dimensional quality evaluation result by the quality evaluation model; the quality judgment result of each dimension represents a quality judgment attribute of the judged news content; the quality evaluation model is obtained by training by using a training text and an artificially labeled quality evaluation result, and the training text is obtained from the same type of contents of the news contents to be evaluated.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a method, a system, a computer device, and a storage medium for evaluating news quality.
Background
With the development of the news industry, the number of news begins to increase on a large scale, and when some news are published, the news do not relate to the interests of the masses, the popularity of news main bodies is low, the news main bodies have a certain distance from audience groups, and news which can arouse the interests of the audiences and cannot reflect objective facts are lacked. Due to the rapid increase of the number of news, the quality of various news is uneven, the news is difficult to classify manually, and no unified framework can automatically classify the news article content with fine granularity by using an artificial intelligent method.
Disclosure of Invention
Based on the above problems, the present application provides a news quality evaluation method, system, computer device, and storage medium, which can automatically evaluate news quality from more detailed dimensions and multiple degrees.
The application discloses following technical scheme:
a first aspect of the present application provides a news quality evaluation method, including:
acquiring a plurality of texts to be evaluated from news contents to be evaluated;
vectorizing the texts to be judged to obtain text vectors;
inputting the text vector into a quality evaluation model, and outputting a multi-dimensional quality evaluation result by the quality evaluation model; the quality evaluation result of each dimension represents a quality judgment attribute of the evaluated news content; the quality evaluation model is obtained by training by using a training text and an artificially labeled quality evaluation result, and the training text is obtained from the same type of contents of the news contents to be evaluated.
In one possible implementation manner, the training method of the quality evaluation model is as follows:
acquiring a training text and a quality judgment result of manual labeling;
vectorizing the training text to obtain a training set content vector;
vectorizing the quality judgment result of the manual annotation to obtain an annotation result vector;
and training the quality evaluation model to be trained according to the training set content vector and the labeling result vector.
In one possible implementation manner, obtaining the quality evaluation results of the training text and the manual label includes:
acquiring a plurality of continuous texts from the same type contents of the news contents to be evaluated as training texts;
acquiring a quality judgment result of manual marking for judging the plurality of continuous texts according to a preset rule; the quality judgment result of the manual annotation comprises multi-dimensional quality judgment attributes.
In one possible implementation, the quality evaluation model includes: a plurality of shared parameter neural networks;
the inputting the text vector into a quality evaluation model, and the quality evaluation model outputting a multi-dimensional quality evaluation result includes:
inputting the text vectors into the plurality of shared parameter neural networks, and outputting a plurality of first result vectors;
inputting the plurality of first result vectors into a plurality of logic operation units for logic operation to obtain a plurality of operation result vectors;
and outputting quality judgment results of multiple dimensions according to the multiple operation result vectors.
In a possible implementation manner, the vectorizing the texts to be evaluated to obtain a text vector includes:
and inputting the text to be evaluated into a pre-trained language representation model, and vectorizing the text to be evaluated by the language representation model to obtain a text vector.
A second aspect of the present application provides a news quality evaluation system, including:
the acquisition unit is used for acquiring a plurality of texts to be evaluated from news contents to be evaluated;
the vectorization unit is used for vectorizing the texts to be judged to obtain text vectors;
the quality evaluation unit is used for inputting the text vector into a quality evaluation model, and the quality evaluation model outputs a multi-dimensional quality evaluation result; the quality judgment result of each dimension represents a quality judgment attribute of the judged news content; the quality evaluation model is obtained by training by using a training text and an artificially labeled quality evaluation result, and the training text is obtained from the same type of contents of the news contents to be evaluated.
In one possible implementation manner, the system further includes a training unit of the quality evaluation model, including:
the training text acquisition unit is used for acquiring a training text and a quality judgment result of manual labeling;
the training text vectorization unit is used for vectorizing the training text to obtain a training set content vector; vectorizing the quality judgment result of the manual annotation to obtain an annotation result vector;
and the training unit is used for training the quality evaluation model to be trained according to the training set content vector and the labeling result vector.
In one possible implementation, the quality evaluation model includes: a plurality of shared parameter neural networks;
the quality evaluation unit is specifically configured to:
inputting the text vectors into the plurality of shared parameter neural networks, and outputting a plurality of first result vectors; inputting the plurality of first result vectors into a plurality of logic operation units for logic operation to obtain a plurality of operation result vectors; and outputting quality judgment results of multiple dimensions according to the multiple operation result vectors.
A third aspect of the present application provides a computer device comprising: memory, processor, and storage
A computer program stored on the memory and executable on the processor, when executing the computer program, implementing a news quality assessment method according to any one of the first aspect of the present application.
A fourth aspect of the present application provides a computer-readable storage medium, which stores instructions that, when executed on a terminal device, cause the terminal device to execute a news quality evaluation method according to any one of the first aspects of the present application.
Compared with the prior art, the method has the following beneficial effects:
the application provides a news quality evaluation method, which comprises the following steps: acquiring a plurality of texts to be evaluated from news contents to be evaluated; vectorizing the texts to be judged to obtain text vectors; inputting the text vector into a quality evaluation model, and outputting a multi-dimensional quality evaluation result by the quality evaluation model; the quality evaluation result of each dimension represents a quality judgment attribute of the evaluated news content; the quality evaluation model is obtained by training by using a training text and an artificially labeled quality evaluation result, and the training text is obtained from the same type of contents of the news contents to be evaluated. Under the condition of not using more computing resources, the model training is carried out by considering the correlation among the plurality of quality judgment attributes, so that the model can automatically evaluate the quality of news with high precision and high efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced 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 that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a news quality evaluation method according to an embodiment of the present application;
FIG. 2 is a flowchart of a training method of a quality assessment model according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of a news quality evaluation process provided in an embodiment of the present application;
fig. 4 is a block diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the drawings are described in detail below.
As described above, as the news industry develops, the number of news starts to increase on a large scale, and some of the news are published and outdated, do not relate to the interests of the broad masses, have low popularity of news subjects, are at a certain distance from audience groups, and lack news which can arouse the interests of the audiences and cannot reflect objective facts. Due to the rapid increase of the number of news, the quality of various news is uneven, the news is difficult to classify manually, and no unified framework can automatically classify the news article content with fine granularity by using an artificial intelligent method.
In view of this, embodiments of the present application provide a news quality evaluation method, a system, a computer device, and a storage medium.
Referring to fig. 1, fig. 1 is a flowchart of a news quality evaluation method provided in an embodiment of the present application. As shown in fig. 1, the method includes:
s110, acquiring a plurality of texts to be evaluated from news contents to be evaluated;
s120, vectorizing the texts to be judged to obtain text vectors;
s130, inputting the text vector into a quality evaluation model, and outputting a multi-dimensional quality evaluation result by the quality evaluation model; the quality evaluation result of each dimension represents a quality judgment attribute of the evaluated news content; the quality evaluation model is obtained by training by using a training text and an artificially labeled quality evaluation result, and the training text is obtained from the same type of contents of the news contents to be evaluated.
In one example, the quality discrimination model is a multi-task learning model.
According to the embodiment of the application, under the condition that more computing resources are not used, the relevance among all granularity classifications is considered for model training, so that the model can automatically classify the news quality in a fine granularity mode with high precision and high efficiency.
Referring to fig. 2, fig. 2 is a flowchart of a quality assessment model training method provided in the embodiment of the present application. As shown in fig. 2, the training method of the quality evaluation model is as follows:
s210, obtaining a training text and a quality judgment result of manual labeling.
S210 includes: and acquiring a plurality of continuous texts from the same type contents of the news contents to be evaluated as training texts. Acquiring a quality judgment result of manual marking for judging the plurality of continuous texts according to a preset rule; the quality judgment result of the manual annotation comprises multi-dimensional quality judgment attributes.
S220, vectorizing the training text to obtain a training set content vector;
s230, vectorizing the quality evaluation result of the manual annotation to obtain an annotation result vector;
s240, training the quality evaluation model to be trained according to the training set content vector and the labeling result vector.
In one example, the process of applying the news quality determination method is as follows:
step 1: preprocessing the news original data to remove some useless contents.
And 2, step: and manually labeling different fine-grained classifications on a small part of the preprocessed data.
And step 3: and training the multi-task model by using manually labeled data, and learning the relevance of classifying the news text into each fine granularity and each fine granularity classification.
And 4, step 4: and taking the other part of preprocessed data as news content to be judged, and obtaining classification of news quality fine granularity through a trained model.
In one example, news content is mapped to a vector in a low-dimensional space, mostly 768 dimensions, by a Bert pre-training model.
Referring to fig. 3, fig. 3 is a schematic diagram of a news quality evaluation process provided in the embodiment of the present application. As shown in fig. 3, after the news text content is obtained, vectorization processing is performed on the news text content through the language representation model, so as to obtain a low-dimensional vectorization representation of the news content. The low-dimensional vector can represent the content of the news in a low-dimensional vector space. The low-dimensional vectors are subjected to an Expert unit (Expert) to learn the correlation among the single tasks, the Expert unit is provided with three network structures e1, e2 and e0 sharing parameters, each network structure is the same, and all the low-dimensional vectors pass through the three network structures sharing the parameters. Meanwhile, the low-dimensional vector representation of news also learns whether the network structure in the expert unit is output or not through six gate control units, then a plurality of outputs controlled by the gate control units are combined, the combined vector is used as the input of a classification unit (Tower), the classification unit classifies different combined vectors, the total number of the classification units is six classification units C0, C1, C2, C3, C4 and C5, and each unit represents a single task. The correlation information between individual tasks is learned in the expert unit and the use of computing resources can be reduced.
In one example, the multi-dimensional quality decision attributes include: a number of fine granularities of news quality are defined, such as timeliness, importance, prominence, proximity, interestingness, and reality.
1. And (3) timeliness: outdated refers to the fact that published information, messages and the like are content which is outdated, are not newly generated, and the reader understands deviation and confuses the real occurrence time of an event due to unclear time, hidden time words or outdated time words and the like.
2. Importance of the method: whether events in news contents have close relations with current life and the vital interests of the masses or not is judged, and the importance of money, life and life of the masses is high.
3. The significance is as follows: the popularity of the event topic in the news content is judged, and the higher the popularity of the subject (including people, groups, places and the like), the higher the significance.
4. The approach is as follows: and judging the distance between the event in the news content and the audience, wherein the distance comprises geographic proximity, close interest relationship and close emotion.
5. Interest: determining news content can appeal to the audience, including the interest of the content and the interest of the manner in which the content is presented.
6. Authenticity: and judging whether the news content can reflect the objective event or not, so that the reader can obtain real information.
In some embodiments, the quality assessment model comprises: a plurality of shared parameter neural networks;
the inputting the text vector into a quality evaluation model, and the quality evaluation model outputting a multi-dimensional quality evaluation result includes:
inputting the text vector into the plurality of shared parameter neural networks, and outputting a plurality of first result vectors;
performing logic operation on the plurality of first result vectors to obtain operation result vectors;
and outputting quality judgment results of multiple dimensions according to the operation result vector.
In some embodiments, the vectorizing the texts to be evaluated to obtain a text vector includes:
and inputting the text to be evaluated into a pre-trained language representation model, and vectorizing the text to be evaluated by the language representation model to obtain a text vector.
The embodiment of the application provides a news quality evaluation system, includes:
the acquisition unit is used for acquiring a plurality of texts to be evaluated from news contents to be evaluated;
the vectorization unit is used for vectorizing the texts to be judged to obtain text vectors;
the quality evaluation unit is used for inputting the text vector into a quality evaluation model, and the quality evaluation model outputs a multi-dimensional quality evaluation result; the quality evaluation result of each dimension represents a quality judgment attribute of the evaluated news content; the quality evaluation model is obtained by training by using a training text and an artificially labeled quality evaluation result, and the training text is obtained from the same type of contents of the news contents to be evaluated.
In one possible implementation, the system further includes a training unit of the quality evaluation model, including:
the training text acquisition unit is used for acquiring a training text and a quality judgment result of manual labeling;
the training text vectorization unit is used for vectorizing the training text to obtain a training set content vector; vectorizing the quality judgment result of the manual annotation to obtain an annotation result vector;
and the training unit is used for training the quality evaluation model to be trained according to the training set content vector and the labeling result vector.
In one possible implementation, the quality evaluation model includes: a plurality of shared parameter neural networks;
the quality evaluation unit is specifically configured to:
inputting the text vector into the plurality of shared parameter neural networks, and outputting a plurality of first result vectors; performing logic operation on the plurality of first result vectors to obtain a plurality of operation result vectors; and outputting quality judgment results of multiple dimensions according to the multiple operation result vectors.
An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a news quality evaluation method according to an embodiment of the present application.
In practice, the computer-readable storage medium may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having 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. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
As shown in fig. 4, a schematic structural diagram of a computer device according to an embodiment of the present application is provided. The computer device 12 shown in FIG. 4 is only an example and should not impose any limitations on the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processor unit 16 executes various functional applications and data processing, such as implementing a news quality evaluation method provided in an embodiment of the present application, by running a program stored in the system memory 28.
It is noted that, as used herein, the term "include" and its variants are intended to be inclusive in an open manner, i.e., "include but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present application are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that, although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A news quality evaluation method is characterized by comprising the following steps:
acquiring a plurality of texts to be evaluated from news contents to be evaluated;
vectorizing the texts to be judged to obtain text vectors;
inputting the text vector into a quality evaluation model, and outputting a multi-dimensional quality evaluation result by the quality evaluation model; the quality evaluation result of each dimension represents a quality judgment attribute of the evaluated news content; the quality evaluation model is obtained by training by using a training text and an artificially labeled quality evaluation result, and the training text is obtained from the same type of contents of the news contents to be evaluated.
2. The method of claim 1, wherein the quality assessment model is trained as follows:
acquiring a training text and a quality judgment result of manual labeling;
vectorizing the training text to obtain a training set content vector;
vectorizing the quality evaluation result of the manual annotation to obtain an annotation result vector;
and training the quality evaluation model to be trained according to the training set content vector and the labeling result vector.
3. The method of claim 2, wherein obtaining the quality evaluation results of the training text and the manual label comprises:
acquiring a plurality of continuous texts from the same type of contents of the news contents to be evaluated as training texts;
acquiring a quality judgment result of manual marking for judging the plurality of continuous texts according to a preset rule; the quality judgment result of the manual annotation comprises multi-dimensional quality judgment attributes.
4. The method of claim 1, wherein the quality assessment model comprises: a plurality of shared parameter neural networks, a plurality of units of logical operation;
the inputting the text vector into a quality evaluation model, and the quality evaluation model outputting a multi-dimensional quality evaluation result includes:
inputting the text vector into the plurality of shared parameter neural networks, and outputting a plurality of first result vectors;
inputting the plurality of first result vectors into a plurality of logic operation units for logic operation to obtain a plurality of operation result vectors;
and outputting quality judgment results of multiple dimensions according to the multiple operation result vectors.
5. The method according to any one of claims 1 to 4, wherein the vectorizing the texts to be evaluated to obtain a text vector comprises:
and inputting the text to be evaluated into a pre-trained language representation model, and vectorizing the text to be evaluated by the language representation model to obtain a text vector.
6. A news quality evaluation system, comprising:
the acquisition unit is used for acquiring a plurality of texts to be evaluated from news contents to be evaluated;
the vectorization unit is used for vectorizing the texts to be judged to obtain text vectors;
the quality evaluation unit is used for inputting the text vector into a quality evaluation model, and the quality evaluation model outputs a multi-dimensional quality evaluation result; the quality evaluation result of each dimension represents a quality judgment attribute of the evaluated news content; the quality evaluation model is obtained by training by using a training text and an artificially labeled quality evaluation result, and the training text is obtained from the same type of contents of the news contents to be evaluated.
7. The system of claim 6, further comprising a training unit of a quality assessment model, comprising:
the training text acquisition unit is used for acquiring a training text and a quality judgment result of manual labeling;
the training text vectorization unit is used for vectorizing the training text to obtain a training set content vector; vectorizing the quality evaluation result of the manual annotation to obtain an annotation result vector;
and the training unit is used for training the quality evaluation model to be trained according to the training set content vector and the labeling result vector.
8. The system of claim 6, wherein the quality assessment model comprises: a plurality of shared parameter neural networks;
the quality evaluation unit is specifically configured to:
inputting the text vector into the plurality of shared parameter neural networks, and outputting a plurality of first result vectors; inputting the plurality of first result vectors into a plurality of logic operation units for logic operation to obtain a plurality of operation result vectors; and outputting quality judgment results of multiple dimensions according to the multiple operation result vectors.
9. A computer device, comprising: memory, processor, and storage
A computer program running on the memory and on the processor, the processor when executing the computer program implementing the news quality judgment method of any one of claims 1-5.
10. A computer-readable storage medium having stored therein instructions that, when executed on a terminal device, cause the terminal device to execute the news quality assessment method according to any one of claims 1-5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107220355A (en) * | 2017-06-02 | 2017-09-29 | 北京百度网讯科技有限公司 | News Quality estimation method, equipment and storage medium based on artificial intelligence |
| US20190287142A1 (en) * | 2018-02-12 | 2019-09-19 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method, apparatus for evaluating review, device and storage medium |
| CN114780712A (en) * | 2022-04-06 | 2022-07-22 | 科技日报社 | Quality evaluation-based news topic generation method and device |
| CN115017404A (en) * | 2022-04-27 | 2022-09-06 | 昆明理工大学 | Target news topic abstracting method based on compressed space sentence selection |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107220355A (en) * | 2017-06-02 | 2017-09-29 | 北京百度网讯科技有限公司 | News Quality estimation method, equipment and storage medium based on artificial intelligence |
| US20190287142A1 (en) * | 2018-02-12 | 2019-09-19 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method, apparatus for evaluating review, device and storage medium |
| CN114780712A (en) * | 2022-04-06 | 2022-07-22 | 科技日报社 | Quality evaluation-based news topic generation method and device |
| CN115017404A (en) * | 2022-04-27 | 2022-09-06 | 昆明理工大学 | Target news topic abstracting method based on compressed space sentence selection |
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