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arXiv · 2305.08428

Legal Extractive Summarization of U.S. Court Opinions

Emmanuel Bauer·Dominik Stammbach·Nianlong Gu·Elliott Ash
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Abstract

This paper tackles the task of legal extractive summarization using a dataset of 430K U.S. court opinions with key passages annotated. According to automated summary quality metrics, the reinforcement-learning-based MemSum model is best and even out-performs transformer-based models. In turn, expert human evaluation shows that MemSum summaries effectively capture the key points of lengthy court opinions. Motivated by these results, we open-source our models to the general public. This represents progress towards democratizing law and making U.S. court opinions more accessible to the general public.

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Emmanuel Bauer, Dominik Stammbach, Nianlong Gu, Elliott Ash. 2023-05-15. Legal Extractive Summarization of U.S. Court Opinions. https://arxiv.org/abs/2305.08428

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