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Legal Extractive Summarization of U.S. Court Opinions
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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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Cited by 1 Pith paper
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CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions
CaseSumm, a 25.6K-pair dataset of Supreme Court opinions and official syllabuses, shows automated metrics favor fine-tuned Mistral while human experts prefer GPT-4, and LLM judges do not align with humans better than ROUGE.
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