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In this paper, we treat sentence annotation as a classification task. We employ sequence-to-sequence models to take sentence position information into account in identifying case law sentences as issues, conclusions, or reasons. We also compare the legal domain specific sentence embedding with other general purpose sentence embeddings to gauge the effect of legal domain knowledge, captured during pre-training, on text classification. We deployed the models on both summaries and full-text decisions. We found that the sentence position information is especially useful for full-text sentence classification. We also verified that legal domain specific sentence embeddings perform better, and that meta-sentence embedding can further enhance performance when sentence position information is included. 

In this paper, we assess the use of several deep learning classification algorithms as a step toward automatically preparing succinct summaries of legal decisions. Short case summaries that tease out the decision’s argument structure by making explicit its issues, conclusions, and reasons (i.e., argument triples) could make it easier for the lay public and legal professionals to gain an insight into what the case is about. We have obtained a sizeable dataset of expert-crafted case summaries paired with full texts of the decisions issued by various Canadian courts. As the manual annotation of the full texts is prohibitively expensive, we explore various ways of leveraging the existing longer summaries which are much less time-consuming to annotate. We compare the performance of the systems trained on the annotations that are manually ported to the full texts from the summaries to the performance of the same systems trained on annotations that are projected from the summaries automatically. The results show the possibility of pursuing the automatic annotation in the future.