WO2022078308A1 - Procédé et appareil de production d'abrégés de documents d'évaluation, dispositif électronique et support de stockage lisible - Google Patents

Procédé et appareil de production d'abrégés de documents d'évaluation, dispositif électronique et support de stockage lisible Download PDF

Info

Publication number
WO2022078308A1
WO2022078308A1 PCT/CN2021/123175 CN2021123175W WO2022078308A1 WO 2022078308 A1 WO2022078308 A1 WO 2022078308A1 CN 2021123175 W CN2021123175 W CN 2021123175W WO 2022078308 A1 WO2022078308 A1 WO 2022078308A1
Authority
WO
WIPO (PCT)
Prior art keywords
paragraph
template
abstract
short sentence
category
Prior art date
Application number
PCT/CN2021/123175
Other languages
English (en)
Chinese (zh)
Inventor
曹辰捷
徐国强
陈家豪
Original Assignee
深圳壹账通智能科技有限公司
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 深圳壹账通智能科技有限公司 filed Critical 深圳壹账通智能科技有限公司
Publication of WO2022078308A1 publication Critical patent/WO2022078308A1/fr

Links

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
    • G06F16/35—Clustering; Classification
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/194—Calculation of difference between files
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking

Definitions

  • the present application relates to the technical field of artificial intelligence, and in particular, to a method, device, electronic device, and readable storage medium for generating a summary of a judgment document.
  • the method for generating the abstract of the judgment document provided in this application includes:
  • the present application also provides a device for generating a summary of a judgment document, the device comprising:
  • a parsing module configured to parse a user's request for generating a judgment document summary based on the client, and obtain the judgment document carried by the request;
  • the input module is used to input the judgment document into the trained paragraph category recognition model, and obtain the paragraph category of each paragraph in the judgment document, the paragraph category includes the first category and the second category, and the judgment document is A collection of paragraphs of the first category as a paragraph set;
  • a matching module configured to perform similarity matching between each paragraph in the paragraph set and each short sentence template in the preconfigured summary template, to obtain a target short sentence template corresponding to each paragraph in the paragraph set;
  • the splicing module is used to input each paragraph in the paragraph set and its corresponding target short sentence template into the trained summary generation model, and obtain the target abstract short sentence corresponding to each paragraph in the paragraph set, according to the target short sentence corresponding to each paragraph
  • the position sequence of the template in the abstract template splices the target abstract short sentences to obtain the abstract text corresponding to the judgment document.
  • the present application also provides an electronic device, the electronic device comprising:
  • the memory stores a judgment document summary generation program executable by the at least one processor, and the judgment document summary generation program is executed by the at least one processor, so that the at least one processor can perform the following steps:
  • the present application also provides a computer-readable storage medium, where a program for generating a summary of a judgment document is stored thereon, and the program for generating a summary of a judgment document can be executed by one or more processors to implement the following steps:
  • FIG. 1 is a schematic flowchart of a method for generating a judgment document abstract according to an embodiment of the present application
  • FIG. 2 is a schematic block diagram of an apparatus for generating a summary of a judgment document provided by an embodiment of the present application
  • FIG. 3 is a schematic structural diagram of an electronic device for implementing a method for generating a judgment document summary provided by an embodiment of the present application;
  • the embodiments of the present application may acquire and process related data based on artificial intelligence technology.
  • Artificial Intelligence is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .
  • the basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics.
  • Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning/deep learning.
  • the present application provides a method for generating an abstract of a judgment document.
  • FIG. 1 a schematic flowchart of a method for generating a summary of a judgment document provided by an embodiment of the present application is shown.
  • the method may be performed by an electronic device, which may be implemented by software and/or hardware.
  • the method for generating a summary of a judgment document includes:
  • the paragraph categories include the first category and the second category, and the first category in the judgment document is A collection of paragraphs as a paragraph set.
  • the length of judgment documents is mainly distributed between 2000 and 8000 words, and the length of abstracts is mainly distributed between 200 and 600 words.
  • the current Chinese generation model cannot accommodate such a huge input and output. Paragraphs get a set of passages to compress the scale of information input to the summary generation model.
  • the paragraph category recognition model is a roberta-large-wwm model, which is used to determine whether each paragraph in the input judgment document belongs to the first category or the second category, where the first category is an important paragraph and the second category is an ordinary paragraph.
  • the roberta-large-wwm model is a derivative of the BERT-large model and contains 24 layers of transformers, 16 attention heads, and 1024 hidden layer units.
  • the training process of the paragraph category recognition model includes:
  • A1 Obtain multiple preset indexes corresponding to the paragraph categories of the judgment document, and mark the paragraph category for the first judgment document sample in the first database based on the multiple preset indexes;
  • the preset indicators include: the relationship between the plaintiff and the court, the plaintiff's claim, the court's opinion, the focus of the dispute, the statement and opinion of the legal facts, and the trial result.
  • the paragraphs associated with the above-mentioned six preset indicators in the first judgment document sample are marked as the first category (important paragraphs), and other paragraphs are marked as the second category (ordinary paragraphs).
  • A3. Determine the true paragraph category of each paragraph in the first referee text sample based on the annotation information, and determine the structural parameters of the paragraph category recognition model by minimizing the loss value between the predicted paragraph category and the true paragraph category, Get the trained paragraph category recognition model.
  • qi is the predicted paragraph category of the ith paragraph in the first judgment document sample
  • pi is the actual paragraph category of the ith paragraph in the first judgment document sample
  • c is the total number of paragraphs in the first judgment document sample
  • loss(q i , p i ) is the loss value between the predicted paragraph category and the real paragraph category of the i-th paragraph in the first referee document sample.
  • a preset threshold for example, 0.7
  • the important paragraphs in the judgment document are extracted by the paragraph category recognition model, which compresses the information scale, avoids the information input to the summary generation model being too long and overflows, and ensures the integrity of the input information of the summary generation model, so that the summary generation model The resulting summary is more accurate.
  • paragraphs in the paragraph set may still have redundant information (some paragraphs may have more than 500 words), and these paragraphs are not necessarily coherent before and after, and cannot be directly spliced as an abstract.
  • a summary template is preconfigured (the summary template includes the above-mentioned 6 preset indicators), and an example of the summary template is as follows: the plaintiff and the court have a relationship of XXXX. The plaintiff filed a petition and ordered the court to pay.... The court argued that the plaintiff's claim had no factual and legal basis, and upon finding out... This court supports the plaintiff's above request. According to Article X of the "Contract Law of the People's Republic of China" ... judgment, 1. The court shall pay the plaintiff XX fees. 2. To reject the plaintiff's other claims. If the obligation to pay money is not fulfilled within the period specified in this judgment, double the interest on the debt for the period of delay in performance.
  • the similarity matching is performed between each paragraph in the paragraph set and each short sentence template in the pre-configured summary template, and the target short sentence template corresponding to each paragraph in the paragraph set is obtained, including:
  • pi is the ith paragraph in the paragraph set
  • a j is the jth short sentence template in the abstract template
  • LCS(pi ,a j ) is the ith paragraph in the paragraph set and the jth short sentence template in the abstract template
  • the length of the longest common subsequence of , len(a j ) is the length of the j-th sentence template in the abstract template
  • len(pi ) is the length of the i - th paragraph in the paragraph set
  • LCSR(pi , a j ) is The upper limit of the length ratio of the longest common subsequence between the i-th paragraph in the paragraph set and the j-th sentence template in the abstract template
  • LCSP(pi ,a j ) is the i -th paragraph in the paragraph set and the j-th short sentence in the abstract template
  • the method further includes:
  • the short sentence template is used as the target short sentence template corresponding to the specified paragraph.
  • the method further includes:
  • the method further includes:
  • the summary generation model is also a roberta-large-wwm model, which is used to generate summary text according to paragraph information.
  • the paragraph category recognition model in this scheme is different from the input sample of the summary generation model, the training target is different, and the model parameters obtained by training are also different.
  • the training process of the abstract generation model includes:
  • C3. Determine the structural parameters of the summary generation model by minimizing the loss value between the real content corresponding to the mask and the predicted content, so as to obtain a trained summary generation model.
  • the abstract generation model predicts the probability distribution of the next token by using all the preceding tokens (words) in each second referee document sample.
  • words preceding tokens
  • this training task in order to meet the abstract generation, a piece of text content is reserved as a known text (25% to 75% of the content of each second judgment document sample), and another part of the text content (75% to 25% of the content of each second judgment document sample) is covered by masking characters.
  • the judgment document is input into the trained paragraph category recognition model, and the paragraph category of each paragraph in the judgment document is obtained, and the paragraph category includes the first category (that is, the first category).
  • important paragraphs and the second category (that is, ordinary paragraphs)
  • the set of paragraphs in the first category in the judgment document is used as the paragraph set.
  • the important paragraphs in the judgment document are extracted and put into the paragraph set through the paragraph category recognition model.
  • the information scale avoids the situation that the information in the subsequent input summary generation model is too long and overflows, causing the subsequent generated summary information to be incomplete and inaccurate;
  • the short sentence template performs similarity matching to obtain the target short sentence template corresponding to each paragraph in the paragraph set. This step further compresses the information scale by matching the similarity between the paragraphs in the paragraph set and the short sentence template in the abstract template;
  • Each paragraph in the paragraph set and its corresponding target short sentence template are input into the trained summary generation model, and the target short sentence corresponding to each paragraph in the paragraph set is obtained.
  • the target abstract sentences are spliced together to obtain the abstract text corresponding to the judgment document.
  • the target abstract sentences are spliced according to the positional order of the target short sentence templates corresponding to each paragraph in the abstract template, so as to ensure the coherence of the abstract. . Therefore, this application ensures the coherence and accuracy of the abstract of the judgment document.
  • FIG. 2 it is a schematic block diagram of an apparatus for generating a summary of a judgment document provided by an embodiment of the present application.
  • the apparatus 100 for generating a summary of a judgment document described in this application may be installed in an electronic device. According to the functions implemented, the apparatus 100 for generating a summary of a judgment document may include a parsing module 110 , an input module 120 , a matching module 130 and a splicing module 140 .
  • the modules described in this application may also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can perform fixed functions, and are stored in the memory of the electronic device.
  • each module/unit is as follows:
  • the parsing module 110 is configured to parse a request for generating a judgment document summary sent by a user based on the client, and obtain the judgment document carried by the request;
  • the input module 120 is used to input the judgment document into the trained paragraph category recognition model to obtain the paragraph category of each paragraph in the judgment document, the paragraph categories include the first category and the second category, and the judgment document is The collection of paragraphs in the first category is referred to as a paragraph set.
  • the length of judgment documents is mainly distributed between 2000 and 8000 words, and the length of abstracts is mainly distributed between 200 and 600 words.
  • the current Chinese generation model cannot accommodate such a huge input and output. Paragraphs get a set of passages to compress the scale of information input to the summary generation model.
  • the paragraph category recognition model is a roberta-large-wwm model, which is used to determine whether each paragraph in the input judgment document belongs to the first category or the second category, where the first category is an important paragraph and the second category is an ordinary paragraph.
  • the roberta-large-wwm model is a derivative of the BERT-large model and contains 24 layers of transformers, 16 attention heads, and 1024 hidden layer units.
  • the training process of the paragraph category recognition model includes:
  • A1 Obtain multiple preset indexes corresponding to the paragraph categories of the judgment document, and mark the paragraph category for the first judgment document sample in the first database based on the multiple preset indexes;
  • the preset indicators include: the relationship between the plaintiff and the court, the plaintiff's claim, the court's opinion, the focus of the dispute, the statement and opinion of the legal facts, and the trial result.
  • the paragraphs associated with the above-mentioned six preset indicators in the first judgment document sample are marked as the first category (important paragraphs), and other paragraphs are marked as the second category (ordinary paragraphs).
  • A3. Determine the true paragraph category of each paragraph in the first referee text sample based on the annotation information, and determine the structural parameters of the paragraph category recognition model by minimizing the loss value between the predicted paragraph category and the true paragraph category, Get the trained paragraph category recognition model.
  • qi is the predicted paragraph category of the ith paragraph in the first judgment document sample
  • pi is the actual paragraph category of the ith paragraph in the first judgment document sample
  • c is the total number of paragraphs in the first judgment document sample
  • loss(q i , p i ) is the loss value between the predicted paragraph category and the real paragraph category of the i-th paragraph in the first referee document sample.
  • a preset threshold for example, 0.7
  • the important paragraphs in the judgment document are extracted by the paragraph category recognition model, which compresses the information scale, avoids the information input to the summary generation model being too long and overflows, and ensures the integrity of the input information of the summary generation model, so that the summary generation model The resulting summary is more accurate.
  • the matching module 130 is configured to perform similarity matching between each paragraph in the paragraph set and each short sentence template in the preconfigured abstract template, to obtain the target short sentence template corresponding to each paragraph in the paragraph set;
  • paragraphs in the paragraph set may still have redundant information (some paragraphs may have more than 500 words), and these paragraphs are not necessarily coherent before and after, and cannot be directly spliced as an abstract.
  • a summary template is preconfigured (the summary template includes the above-mentioned 6 preset indicators), and an example of the summary template is as follows: the plaintiff and the court have a relationship of XXXX. The plaintiff filed a petition and ordered the court to pay.... The court argued that the plaintiff's claim had no factual and legal basis, and upon finding out... This court supports the plaintiff's above request. According to Article X of the "Contract Law of the People's Republic of China" ... judgment, 1. The court shall pay the plaintiff XX fees. 2. To reject the plaintiff's other claims. If the obligation to pay money is not fulfilled within the period specified in this judgment, double the interest on the debt for the period of delay in performance.
  • the similarity matching is performed between each paragraph in the paragraph set and each short sentence template in the pre-configured summary template, and the target short sentence template corresponding to each paragraph in the paragraph set is obtained, including:
  • pi is the ith paragraph in the paragraph set
  • a j is the jth short sentence template in the abstract template
  • LCS(pi ,a j ) is the ith paragraph in the paragraph set and the jth short sentence template in the abstract template
  • the length of the longest common subsequence of , len(a j ) is the length of the j-th sentence template in the abstract template
  • len(pi ) is the length of the i - th paragraph in the paragraph set
  • LCSR(pi , a j ) is The upper limit of the length ratio of the longest common subsequence between the i-th paragraph in the paragraph set and the j-th sentence template in the abstract template
  • LCSP(pi ,a j ) is the i -th paragraph in the paragraph set and the j-th short sentence in the abstract template
  • the matching module 130 is also used for:
  • the short sentence template is used as the target short sentence template corresponding to the specified paragraph.
  • the matching module 130 is further configured to:
  • the matching module 130 is further configured to:
  • the splicing module 140 is used to input each paragraph in the paragraph set and its corresponding target short sentence template into the trained summary generation model, and obtain the target short sentence corresponding to each paragraph in the paragraph set, according to the target short sentence corresponding to each paragraph.
  • the target abstract short sentences are spliced together according to the position sequence of the sentence template in the abstract template, so as to obtain the abstract text corresponding to the judgment document.
  • the summary generation model is also a roberta-large-wwm model, which is used to generate summary text according to paragraph information.
  • the paragraph category recognition model in this scheme is different from the input sample of the summary generation model, the training target is different, and the model parameters obtained by training are also different.
  • the training process of the abstract generation model includes:
  • C3. Determine the structural parameters of the summary generation model by minimizing the loss value between the real content corresponding to the mask and the predicted content, so as to obtain a trained summary generation model.
  • the abstract generation model predicts the probability distribution of the next token by using all the preceding tokens (words) in each second referee document sample.
  • words preceding tokens
  • this training task in order to meet the abstract generation, a piece of text content is reserved as a known text (25% to 75% of the content of each second judgment document sample), and another part of the text content (75% to 25% of the content of each second judgment document sample) is covered by masking characters.
  • FIG. 3 it is a schematic structural diagram of an electronic device for implementing a method for generating a summary of a judgment document provided by an embodiment of the present application.
  • the electronic device 1 is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions.
  • the electronic device 1 may be a computer, a single network server, a server group composed of multiple network servers, or a cloud based on cloud computing composed of a large number of hosts or network servers, wherein cloud computing is a kind of distributed computing, A super virtual computer consisting of a collection of loosely coupled computers.
  • the electronic device 1 includes, but is not limited to, a memory 11, a processor 12, and a network interface 13 that can be communicatively connected to each other through a system bus.
  • the abstract generation program 10 is executable by the processor 12 .
  • FIG. 3 only shows the electronic device 1 having the components 11-13 and the judgment document abstract generating program 10. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device 1, and may include Fewer or more components than shown, or some components are combined, or a different arrangement of components.
  • the memory 11 includes a memory and at least one type of readable storage medium.
  • the memory provides a cache for the operation of the electronic device 1;
  • the readable storage medium can be, for example, flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM) ), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. non-volatile storage media.
  • the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1; in other embodiments, the non-volatile storage medium may also be an external storage unit of the electronic device 1
  • a storage device such as a pluggable hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card (Flash Card), etc. equipped on the electronic device 1.
  • the readable storage medium of the memory 11 is generally used to store the operating system and various application software installed in the electronic device 1 , for example, to store the code of the judgment document abstract generating program 10 in an embodiment of the present application.
  • the memory 11 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 12 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips.
  • the processor 12 is generally used to control the overall operation of the electronic device 1, such as performing control and processing related to data interaction or communication with other devices.
  • the processor 12 is configured to run the program code or process data stored in the memory 11 , for example, run the judgment document summary generation program 10 and the like.
  • the network interface 13 may include a wireless network interface or a wired network interface, and the network interface 13 is used to establish a communication connection between the electronic device 1 and a client (not shown in the figure).
  • the electronic device 1 may further include a user interface, and the user interface may include a display (Display), an input unit such as a keyboard (Keyboard), and an optional user interface may also include a standard wired interface and a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like.
  • the display may also be appropriately called a display screen or a display unit, which is used for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
  • the judgment document summary generation program 10 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, and when running in the processor 12, can realize:
  • the above-mentioned judgment document abstract generating program 10 by the processor 12, reference may be made to the description of the relevant steps in the corresponding embodiment of FIG. 1, and details are not described herein. It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned judgment documents, the above-mentioned judgment documents can also be stored in a node of a blockchain.
  • the modules/units integrated in the electronic device 1 may be stored in a computer-readable storage medium.
  • the computer-readable storage medium may be non-volatile or non-volatile.
  • the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) ).
  • the computer-readable storage medium stores a judgment document summary generation program 10, and the judgment document summary generation program 10 can be executed by one or more processors to realize the following steps:
  • modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.
  • the blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Machine Translation (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente demande concerne le domaine technique de l'intelligence artificielle. Est divulgué un procédé de production d'un abrégé de document d'évaluation. Le procédé comprend les étapes consistant à : entrer un document d'évaluation dans un modèle instruit d'identification de catégories de paragraphes, afin d'obtenir des catégories de paragraphes de paragraphes du document d'évaluation et prendre un ensemble de paragraphes d'une première catégorie comme ensemble de paragraphes ; réaliser respectivement un appariement de similarités sur chaque paragraphe de l'ensemble de paragraphes et sur un modèle de phrases courtes d'un modèle d'abrégé, afin d'obtenir des modèles de phrases courtes cibles correspondant aux paragraphes de l'ensemble de paragraphes ; et entrer les paragraphes et les modèles de phrases courtes cibles leur correspondant dans un modèle instruit de production d'abrégés, afin d'obtenir des phrases courtes d'abrégés cibles correspondant aux paragraphes de l'ensemble de paragraphes et combiner les phrases courtes d'abrégés cibles selon l'ordre des positions, dans le modèle d'abrégé, des modèles de phrases courtes cibles correspondant aux paragraphes, afin d'obtenir un texte d'abrégé correspondant au document d'évaluation. L'invention concerne en outre un appareil de production d'un abrégé de document d'évaluation, un dispositif électronique et un support de stockage lisible. Grâce à la présente demande, la cohérence et la précision d'un abrégé de document d'évaluation sont garanties.
PCT/CN2021/123175 2020-10-12 2021-10-12 Procédé et appareil de production d'abrégés de documents d'évaluation, dispositif électronique et support de stockage lisible WO2022078308A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011087426.7A CN112182224A (zh) 2020-10-12 2020-10-12 裁判文书摘要生成方法、装置、电子设备及可读存储介质
CN202011087426.7 2020-10-12

Publications (1)

Publication Number Publication Date
WO2022078308A1 true WO2022078308A1 (fr) 2022-04-21

Family

ID=73949353

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/123175 WO2022078308A1 (fr) 2020-10-12 2021-10-12 Procédé et appareil de production d'abrégés de documents d'évaluation, dispositif électronique et support de stockage lisible

Country Status (2)

Country Link
CN (1) CN112182224A (fr)
WO (1) WO2022078308A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116127977A (zh) * 2023-02-08 2023-05-16 中国司法大数据研究院有限公司 一种面向裁判文书的伤亡人数提取方法
CN116188125A (zh) * 2023-03-10 2023-05-30 深圳市伙伴行网络科技有限公司 一种写字楼的招商管理方法、装置、电子设备及存储介质

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112182224A (zh) * 2020-10-12 2021-01-05 深圳壹账通智能科技有限公司 裁判文书摘要生成方法、装置、电子设备及可读存储介质
CN113590809A (zh) * 2021-07-02 2021-11-02 华南师范大学 一种裁判文书摘要自动生成方法及装置
CN113255319B (zh) * 2021-07-02 2021-10-26 深圳市北科瑞声科技股份有限公司 模型训练方法、文本分段方法、摘要抽取方法及装置
CN113704457B (zh) * 2021-07-23 2024-03-01 北京搜狗科技发展有限公司 摘要的生成方法、装置及存储介质
CN113609840B (zh) * 2021-08-25 2023-06-16 西华大学 一种汉语法律判决摘要生成方法及系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106126620A (zh) * 2016-06-22 2016-11-16 北京鼎泰智源科技有限公司 基于机器学习的中文自动文摘方法
US20180213057A1 (en) * 2017-01-26 2018-07-26 Linkedin Corporation Customized profile summaries for online social networks
CN110069623A (zh) * 2017-12-06 2019-07-30 腾讯科技（深圳）有限公司 摘要文本生成方法、装置、存储介质和计算机设备
CN112182224A (zh) * 2020-10-12 2021-01-05 深圳壹账通智能科技有限公司 裁判文书摘要生成方法、装置、电子设备及可读存储介质

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106126620A (zh) * 2016-06-22 2016-11-16 北京鼎泰智源科技有限公司 基于机器学习的中文自动文摘方法
US20180213057A1 (en) * 2017-01-26 2018-07-26 Linkedin Corporation Customized profile summaries for online social networks
CN110069623A (zh) * 2017-12-06 2019-07-30 腾讯科技（深圳）有限公司 摘要文本生成方法、装置、存储介质和计算机设备
CN112182224A (zh) * 2020-10-12 2021-01-05 深圳壹账通智能科技有限公司 裁判文书摘要生成方法、装置、电子设备及可读存储介质

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116127977A (zh) * 2023-02-08 2023-05-16 中国司法大数据研究院有限公司 一种面向裁判文书的伤亡人数提取方法
CN116127977B (zh) * 2023-02-08 2023-10-03 中国司法大数据研究院有限公司 一种面向裁判文书的伤亡人数提取方法
CN116188125A (zh) * 2023-03-10 2023-05-30 深圳市伙伴行网络科技有限公司 一种写字楼的招商管理方法、装置、电子设备及存储介质
CN116188125B (zh) * 2023-03-10 2024-05-31 深圳市伙伴行网络科技有限公司 一种写字楼的招商管理方法、装置、电子设备及存储介质

Also Published As

Publication number Publication date
CN112182224A (zh) 2021-01-05

Similar Documents

Publication Publication Date Title
WO2022078308A1 (fr) Procédé et appareil de production d'abrégés de documents d'évaluation, dispositif électronique et support de stockage lisible
WO2022105115A1 (fr) Procédé et appareil d'appariement de paire de question et réponse, dispositif électronique et support de stockage
WO2022105122A1 (fr) Procédé et appareil de génération de réponse basés sur l'intelligence artificielle, ainsi que dispositif informatique et support
WO2022141861A1 (fr) Procédé et appareil de classification d'émotions, dispositif électronique et support de stockage
WO2021042521A1 (fr) Procédé de génération automatique de contrat, dispositif informatique et support de stockage informatique non volatil
WO2022048211A1 (fr) Procédé et appareil de génération de répertoire de document, dispositif électronique et support de stockage lisible
US20180285326A1 (en) Classifying and ranking changes between document versions
KR20200094627A (ko) 텍스트 관련도를 확정하기 위한 방법, 장치, 기기 및 매체
WO2021135469A1 (fr) Procédé, appareil, dispositif informatique et support d'extraction d'informations basée sur l'apprentissage automatique
WO2022048210A1 (fr) Procédé et appareil de reconnaissance d'entités nommées, dispositif électronique et support de stockage lisible
CN111984793A (zh) 文本情感分类模型训练方法、装置、计算机设备及介质
WO2020259280A1 (fr) Procédé et appareil de gestion de journaux, dispositif réseau et support de stockage lisible
CN112183091A (zh) 问答对生成方法、装置、电子设备及可读存储介质
WO2021196825A1 (fr) Procédé et appareil de génération de résumé, dispositif électronique et support
WO2022105496A1 (fr) Procédé et appareil de mise en relation pour suivi intelligente, et dispositif électronique et support de stockage lisible
WO2022222943A1 (fr) Procédé et appareil de recommandation de département, dispositif électronique et support de stockage
CN109101489A (zh) 一种文本自动摘要方法、装置及一种电子设备
CN111414122A (zh) 一种智能文本处理方法、装置、电子设备及存储介质
CN111797217B (zh) 基于faq匹配模型的信息查询方法、及其相关设备
WO2023178978A1 (fr) Procédé et appareil de révision d'ordonnance basés sur l'intelligence artificielle, dispositif et support
WO2019085118A1 (fr) Procédé d'analyse de mot associé basé sur un modèle de sujet, et appareil électronique et support d'informations
JP2023517518A (ja) ヌル値又は同等の値を有するリレーショナル・テーブルのためのベクトル埋込モデル
CN112668281A (zh) 基于模板的语料自动化扩充方法、装置、设备及介质
WO2023178979A1 (fr) Procédé et appareil d'étiquetage de question, dispositif électronique et support de stockage
CN113486680B (zh) 文本翻译方法、装置、设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21879352

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21879352

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 180723)

122 Ep: pct application non-entry in european phase

Ref document number: 21879352

Country of ref document: EP

Kind code of ref document: A1