Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance
This addresses the time- and cost-intensive need for expert summarization of legal documents, but it is incremental as it builds on existing methods like multi-task learning and MMR.
The paper tackles extractive summarization of legal decisions in low-resource settings by using multi-task learning and maximal marginal relevance to reduce redundancy, achieving ROUGE scores comparable to inter-annotator agreement.
Summarizing legal decisions requires the expertise of law practitioners, which is both time- and cost-intensive. This paper presents techniques for extractive summarization of legal decisions in a low-resource setting using limited expert annotated data. We test a set of models that locate relevant content using a sequential model and tackle redundancy by leveraging maximal marginal relevance to compose summaries. We also demonstrate an implicit approach to help train our proposed models generate more informative summaries. Our multi-task learning model variant leverages rhetorical role identification as an auxiliary task to further improve the summarizer. We perform extensive experiments on datasets containing legal decisions from the US Board of Veterans' Appeals and conduct quantitative and expert-ranked evaluations of our models. Our results show that the proposed approaches can achieve ROUGE scores vis-à-vis expert extracted summaries that match those achieved by inter-annotator comparison.