A High-Precision Two-Stage Legal Judgment Summarization

Yue Huang, Lijuan Sun (), Chong Han and Jian Guo
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Yue Huang: College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Lijuan Sun: College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Chong Han: College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Jian Guo: College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Mathematics, 2023, vol. 11, issue 6, 1-16

Abstract: Legal judgments are generally very long, and relevant information is often scattered throughout the text. To complete a legal judgment summarization, capturing important, relevant information comprehensively from a lengthy text is crucial. The existing abstractive-summarization models based on pre-trained language have restrictions on the length of an input text. Another concern is that the generated summaries have not been well integrated with the legal judgment’s technical terms and specific topics. In this paper, we used raw legal judgments as information of different granularities and proposed a two-stage text-summarization model to handle different granularities of information. Specifically, we treated the legal judgments as a sequence of sentences and selected key sentence sets from the full texts as an input corpus for summary generation. In addition, we extracted keywords related to technical terms and specific topics in the legal texts and introduced them into the summary-generation model as an attention mechanism. The experimental results on the CAIL2020 and the LCRD datasets showed that our model achieved an overall 0.19–0.41 improvement in its ROUGE score, as compared to the baseline models. Further analysis also showed that our method could comprehensively capture essential and relevant information from lengthy legal texts and generate better legal judgment summaries.

Keywords: legal judgment; text summarization; generative network; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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