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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (5): 104-114    DOI: 10.11925/infotech.2096-3467.2020.1109
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Automatic Abstracting Civil Judgment Documents with Two-Stage Procedure
Wang Yizhen,Ou Shiyan(),Chen Jinju
School of Information Management, Nanjing University, Nanjing 210023, China
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Abstract  

[Objective] This paper tries to automatically summarize the contents of civil judgment documents in the first-instance, aiming to provide concise, readable, coherent, accurate and efficient knowledge services. [Methods] We proposed an automatic abstracting method for judgment documents, which includes extractive summary stage and abstract summary stage. We first added the expanded residual gate convolution to the pre-training model to extract key sentences from the judgment documents. Then, we input the extractive summary to the sequence to sequence model and generated the final judgment document abstracts. [Results] The ROUGE indicators of the proposed model were 50.31, 36.60, and 48.86 with the experimental data sets of judgment documents, which were 25.00, 23.25, 24.66 higher than the results of the benchmark model (LEAD-3). [Limitations] The extractive summary obtained in the first stage is used as the input of the second stage abstract model, which creates cumulative error issue. The overall performance of the proposed model is decided by the extractive model of the first stage. [Conclusions] The proposed model could summarize judgment texts automatically, which solve the information overload issue and help users quickly read judgment documents.

Key wordsPre-trained Language Model      Automatic Summary      Judgment Documents      Abstract Summarization      Extractive Summarization     
Received: 11 November 2020      Published: 27 May 2021
ZTFLH:  TP391  
Fund:The work is supported by the National Social Science Foundation of China(17ATQ001)
Corresponding Authors: Ou Shiyan     E-mail: oushiyan@nju.edu.cn

Cite this article:

Wang Yizhen,Ou Shiyan,Chen Jinju. Automatic Abstracting Civil Judgment Documents with Two-Stage Procedure. Data Analysis and Knowledge Discovery, 2021, 5(5): 104-114.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1109     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I5/104

篇章结构组成
成分
功能说明
案件基本信息 记录案件基础信息,包含:文书名、案号、文书性质、裁判日期、审理程序等信息
当事人信息 记录当事人信息,包含:原被告信息简介、原被告类型和数量以及代理律所等信息
审理经过 记录案件审理的过程,包含:案由、原被告名称、案件立案时间等信息
原告诉称 记录原告当事人的诉讼请求和对应诉讼请求的理由等信息
被告辩称 记录被告当事人针对原告当事人的诉讼请求和理由提出反驳的抗辩事由等信息
法院查明 记录法院针对当前案件进行事实、证据调查的结果,查明事实包含详细的证据、事情经过等信息
本院认为 记录裁判文书的说理过程,是法院就案件作出的说理评判信息,包含:当事人双方的争议焦点、案件说理逻辑、引用的法律法规
判决结果 记录案件的详细判决结果,包含原告的权利和义务信息
其他信息 记录审判人员、书记员、判决日期等信息
Explanation of the Chapter Structure and Function of the First-instance of Civil Judgments Document
The Framework of Sentences Structure Function in the First-instance of Civil Judgments Document
Two-Stage Automatic Summarization Model for Judgment Documents
The Extractive Summary Model of Judgment Document
The Abstractive Summary Model of Judgment Document
The Process of Marking the First-instance of Civil Judgments Document
操作系统 GPU Python Cuda Tensorflow-GPU Rouge Keras
Ubuntu 18.04 TITAN RTX 3.6.9 10.0 1.14.0 1.5.5 2.3.1
The Experimental Environments
方法 ROUGE
n=1 n=2 n=L
Baseline LEAD-3 25.31 13.35 24.20
抽取式 NeuSum 44.36 17.79 41.96
BERT+Classifier 45.60 19.99 43.57
BERT+Transformer 47.75 30.81 46.28
生成式 Transformer-Abstractive 44.15 28.62 43.14
Pointer-Generator Networks 47.78 32.82 47.13
Bottom-Up Abstractive 48.01 25.17 46.47
本文模型 TSSM-Extractive 49.69 31.94 48.85
TSSM 50.31 36.60 48.86
Control Experimental Results
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