A Comprehensive Analysis of Indian Legal Documents Summarization Techniques

In the Legal AI field, the summarization of legal documents is very challenging. Since the Indian case documents are much noisier and poorly organized, the summarization of legal documents can be useful for legal professionals, who often have to read and analyze large amounts of legal text. During the review process of the legal documents, a team of reviewers may be needed to understand and for taking further actions. A branch of text summarization called ‘legal text summarization’ which is concerned with summarizing legal texts, such as court opinions, contracts, and legal briefs may reduce the need of these reviewers. Legal text summarization aims to highlight the key points of a legal document and convey them in a concise form so that decisions can be made in quick manner. In this paper, we experimented on seven machine learning-based summarization models to analyse their performance on judgment report datasets that has been collected from Indian national legal portal. The models that are taken here for the analysis are BART, LexRank, TextRank, Luhn, LSA, Legal Pegasus, and Longformer. We experimented with these models to find which model may perform well on the legal data. As a result, we observed that Legal Pegasus outperforms over all other models in the case legal summarization.