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Enhancing legal judgment summarization with integrated semantic and structural information

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Abstract

Legal Judgment Summarization (LJS) can highly summarize legal judgment documents, improving judicial work efficiency in case retrieval and other occasions. Legal judgment documents are usually lengthy; however, most existing LJS methods are directly based on general text summarization models, which cannot handle long texts effectively. Additionally, due to the complex structural characteristics of legal judgment documents, some information may be lost by applying only one single kind of summarization model. To address these issues, we propose an integrated summarization method which leverages both semantic and structural information to improve the quality of LJS. Specifically, legal judgment documents are firstly segmented into three relatively short parts according to their specific structure. We propose an extractive summarization model named BSLT and an abstractive summarization model named LPGN by adopting Lawformer as the encoder. Lawformer is a new pre-trained language model for long legal documents, which specializes in capturing long-distance dependency and modeling legal semantic features. Then, we adopt different models to summarize the corresponding part regarding its structural characteristics. Finally, the obtained summaries are integrated to generate a high-quality summary involving semantic and structural information. We conduct comparative experiments to evaluate the performance of our model. The results show that our model outperforms the baseline model LEAD-3 by 14.78% on the mean ROUGE score, which demonstrates our method is effective in LJS and is prospected to be applied to assist other tasks in legal artificial intelligence.

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Notes

  1. http://cail.cipsc.org.cn/.

  2. https://github.com/china-ai-law-challenge/CAIL2020/tree/master/sfzy/baseline.

  3. https://github.com/YunwenTechnology/Unilm

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Acknowledgements

This work was supported by Humanities and Social Science Planning Fund [Grant No. 21YJAZH013] from the Ministry of Education, China.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, China

    Jingpei Dan & Weixuan Hu

  2. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Yuming Wang

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  1. Jingpei Dan
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  2. Weixuan Hu
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  3. Yuming Wang
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Correspondence to Jingpei Dan or Yuming Wang.

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Appendix: The English version of the example in case study

Appendix: The English version of the example in case study

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Dan, J., Hu, W. & Wang, Y. Enhancing legal judgment summarization with integrated semantic and structural information. Artif Intell Law (2023). https://doi.org/10.1007/s10506-023-09381-8

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