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A challenging task when generating summaries of legal documents is the ability to address their argumentative nature. We introduce a simple technique to capture the argumentative structure of legal documents by integrating argument role labeling into the summarization process. Experiments with pretrained language models show that our proposed approach improves performance over strong baselines. 

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. 

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 they have been used in the 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 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. 

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.