Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 331-336.doi: 10.11896/jsjkx.210500028

• Artificial Intelligence • Previous Articles     Next Articles

Abstractive Automatic Summarizing Model for Legal Judgment Documents

ZHOU Wei1, WANG Zhao-yu1, WEI Bin2   

  1. 1 School of Information Management for Law,China University of Political Science and Law,Beijing 102249,China
    2 Institute of Digital Jurisprudence,Zhejiang University,Hangzhou 310008,China
  • Received:2021-05-06 Revised:2021-07-15 Online:2021-12-15 Published:2021-11-26
  • About author:ZHOU Wei,born in 1985,assistant professor,Ph.D.His main research in-terests include legal service and judicial management technology,and legal information management.
    WEI Bin,born in 1986,professor of Hundred Talents Program,Ph.D supervisor,is a member of China Computer Federation.His main research interests include AI & Law,knowledge representation and legal logic.
  • Supported by:
    Research and Innovation Project of CUPL(21ZFQ82005),Key R & D Program of Zhejiang Province (2020C01060),Key R & D Projects of the Ministry of Science and Technology(2018YFC0831800),Key Project of National Social Science Foundation(20&ZD047) and Fundamental Research Funds for the Central Universities.

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Abstract: At present,the automatic summarization model for Chinese content applied to legal judgement documents mainly adopts the extraction method.However,due to the lengthiness and low level of structure of legal texts,the accuracy and reliability of extraction method is insufficient for practical application.In order to obtain high quality summaries of legal judgment documents,in this paper,we propose an abstractive automatic summarization model based on multi-model fusion.Based on Seq2Seq model,we apply attention mechanism and selective gates to better process the data input.Specifically,we combine Bert pre-trai-ning and reinforcement learning policy to optimize our model.The corpus we built consists of 50 000 legal judgment documents regarding small claims procedure and summary procedure.Evaluations on the corpus demonstrate that the proposed model outperforms all of the baseline model,and the mean ROUGE score is 5.81% higher than that of conventional Seq2seq+Attentionmodel.

Key words: Attention mechanism, Automatic summarization, Judgement documents, Model fusion, Reinforcement lear-ning, Seq2Seq

CLC Number: 

  • TP18
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