GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis

TL;DR

GLARE is an unsupervised method that improves legal document classification by generating summaries and ranking relevant topics without needing labeled data, aiding legal analysis of special appeals.

Contribution

It introduces Guided LexRank, a modified graph-based summarization algorithm, tailored for legal texts, eliminating the need for labeled training data.

Findings

Effective in classifying special appeals

No prior labeled data required

Matches expert human classification

Abstract

The Brazilian Constitution, known as the Citizen's Charter, provides mechanisms for citizens to petition the Judiciary, including the so-called special appeal. This specific type of appeal aims to standardize the legal interpretation of Brazilian legislation in cases where the decision contradicts federal laws. The handling of special appeals is a daily task in the Judiciary, regularly presenting significant demands in its courts. We propose a new method called GLARE, based on unsupervised machine learning, to help the legal analyst classify a special appeal on a topic from a list made available by the National Court of Brazil (STJ). As part of this method, we propose a modification of the graph-based LexRank algorithm, which we call Guided LexRank. This algorithm generates the summary of a special appeal. The degree of similarity between the generated summary and different topics is evaluated using the BM25 algorithm. As a result, the method presents a ranking of themes most appropriate to the analyzed special appeal. The proposed method does not require prior labeling of the text to be evaluated and eliminates the need for large volumes of data to train a model. We evaluate the effectiveness of the method by applying it to a special appeal corpus previously classified by human experts.