Organizing Portuguese Legal Documents through Topic Discovery

Abstract

A significant challenge in the legal domain is to organize and summarize a constantly growing collection of legal documents, uncovering hidden topics, or themes, that later can support tasks such as legal case retrieval and legal judgment prediction. This massive amount of digital legal documents, combined with the inherent complexity of judiciary systems worldwide, presents a promising scenario for Machine Learning solutions, mainly those taking advantage of all the advancements in the area of Natural Language Processing (NLP). It is in this scenario that Jusbrasil, the largest legal tech company in Brazil, is situated. Using a dataset partially curated by the Jusbrasil legal team, we explore topic modeling solutions using state of the art language models, trained with legal Portuguese documents, to automatically organize and summarize this complex collection of documents. Instead of using an entire legal case, which usually is composed of many pages, we show that it is possible to efficiently organize the collection using the syllabus (in Portuguese, ementa jurisprudencial) from each court decision as they concisely summarize the main points presented by the entire decision.