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Legal texts routinely use concepts that are difficult to understand. Lawyerselaborate on the meaning of such concepts by, among other things, carefullyinvestigating how have they been used in past. Finding text snippets thatmention a particular concept in a useful way is tedious, time-consuming, and,hence, expensive. We assembled a data set of 26,959 sentences, coming fromlegal case decisions, and labeled them in terms of their usefulness forexplaining selected legal concepts. Using the dataset we study theeffectiveness of transformer-based models pre-trained on large language corporato detect which of the sentences are useful. In light of models' predictions,we analyze various linguistic properties of the explanatory sentences as wellas their relationship to the legal concept that needs to be explained. We showthat the transformer-based models are capable of learning surprisinglysophisticated features and outperform the prior approaches to the task.

Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models