Olivier Salaün, Frédéric Piedboeuf, Guillaume Le Berre, David Alfonso-Hermelo, Philippe Langlais. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Topic modeling is widely used in various domains for extracting latent topics underlying large corpora, including judicial texts. In the latter, topics tend to be made by and for domain experts, but remain unintelligible for laymen. In the framework of housing law court decisions in French which mixes abstract legal terminology with real-life situations described in common language, similarly to [1], we aim at identifying different situations that can cause a tenant to prosecute their landlord in court with the application of topic models. Upon quantitative evaluation, LDA and BERTopic deliver the best results, but a closer manual analysis reveals that the second embedding-based approach is much better at producing and even uncovering topics that describe a tenant’s real-life issues and situations.

Legal text summarization is generally formalized as an extractive text summarization task applied to court decisions from which the most relevant sentences are identified and returned as a gist meant to be read by legal experts. However, such summaries are not suitable for laymen seeking intelligible legal information. In the scope of the JusticeBot, a question-answering system in French that provides information about housing law, we intend to generate summaries of court decisions that are, on the one hand, conditioned by a question-answer-decision triplet, and on the other hand, intelligible for ordinary citizens not familiar with legal documents. So far, our best model, a further pre-trained BARThez, achieves an average ROUGE-1 score of 37.7 and a deepened manual evaluation of summaries reveals that there is still room for improvement.

In recent years, transformer [4] and BERT models [1] have been widely used in plain NLP tasks with the assumption that models first pretrained on massive corpora then fine-tuned on the dataset of a given task may suffice to achieve significant improvements. At the intersection of machine learning and law, legal judgment prediction (LJP) is a task that aims at predicting the outcome of a lawsuit based on a representation of the case. Such task is usually formalized in NLP as a text classification with different classes or labels corresponding to the verdicts. One specificity of court rulings is that their decisions are based on the application of legal articles to the facts described by the two parties (applicant and defendant).