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Issue Area Discovery from Legal Opinion Summaries using Neural Text Processing

Applying existed methods of language technology for classifying judicial opinions into their respective issue areas, often requires annotation voting made by human experts. A tedious task nonetheless, further exacerbated by legal descriptions consisting of long text sequences that not necessarily conform to plain English linguistics or grammar patterns. In this paper, we propose instead a succinct representation of an opinion summary joined by case-centered meta-data to form a docket entry. We assembled over a thousand entries from court cases to render our low-resourced target legal domain, and avoided optimistic performance estimates by applying adversarial data split that ensures the most dissimilar train and test sets. Surprisingly, our experimental results show that fine-tuning a pretrained model on standard English recovers issue area prediction by 9 and 8 F1 percentage points over a pretrained model on the legal domain, for macro and weighted average scores, respectively.