  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-\`a-vis expert extracted summaries that match those achieved by
inter-annotator comparison.
