  Legal texts routinely use concepts that are difficult to understand. Lawyers
elaborate on the meaning of such concepts by, among other things, carefully
investigating how have they been used in past. Finding text snippets that
mention a particular concept in a useful way is tedious, time-consuming, and,
hence, expensive. We assembled a data set of 26,959 sentences, coming from
legal case decisions, and labeled them in terms of their usefulness for
explaining selected legal concepts. Using the dataset we study the
effectiveness of transformer-based models pre-trained on large language corpora
to detect which of the sentences are useful. In light of models' predictions,
we analyze various linguistic properties of the explanatory sentences as well
as their relationship to the legal concept that needs to be explained. We show
that the transformer-based models are capable of learning surprisingly
sophisticated features and outperform the prior approaches to the task.
