Papers › Legal Extractive Summarization of U.S. Court Opinions
Legal Extractive Summarization of U.S. Court Opinions
Emmanuel Bauer, Dominik Stammbach, Nianlong Gu, Elliott Ash
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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