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.