Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance. (arXiv:2210.12437v1 [cs.CL])

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 …

arxiv decisions legal multi-task learning summarization