Automatic Summarization of Legal Decisions using Iterative Masking of Predictive Sentences

Abstract

We report on a pilot experiment in automatic, extractive summarization of legal cases concerning Post-traumatic Stress Disorder from the US Board of Veterans' Appeals. We hypothesize that length-constrained extractive summaries benefit from choosing among sentences that are predictive for the case outcome. We develop a novel train-attribute-mask pipeline using a CNN classifier to iteratively select predictive sentences from the case, which measurably improves prediction accuracy on partially masked decisions. We then select a subset for the summary through type classification, maximum marginal relevance, and a summarization template. We use ROUGE metrics and a qualitative survey to evaluate generated summaries along with expert-extracted and expert-drafted summaries. We show that sentence predictiveness does not reliably cover all decision-relevant aspects of a case, illustrate that lexical overlap metrics are not well suited for evaluating legal summaries, and suggest that future work should focus on case-aspect coverage.