Automatic Text Summarization of Legal Cases: A Hybrid Approach

TL;DR

This paper proposes a hybrid NLP method combining k-means clustering and tf-idf for automatic summarization of legal cases, aiming to reduce manual effort and improve summary quality.

Contribution

It introduces a novel hybrid approach for legal case summarization using clustering and tf-idf, with evaluation against lawyer-prepared summaries.

Findings

The method achieves competitive ROUGE scores compared to manual summaries.

Clustering effectively identifies key sentences in legal texts.

Suggestions for enhancing the summarization process are discussed.

Abstract

Manual Summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain. Lawyers spend a lot of time preparing legal briefs of their clients' case files. Automatic Text summarization is a constantly evolving field of Natural Language Processing(NLP), which is a subdiscipline of the Artificial Intelligence Field. In this paper a hybrid method for automatic text summarization of legal cases using k-means clustering technique and tf-idf(term frequency-inverse document frequency) word vectorizer is proposed. The summary generated by the proposed method is compared using ROGUE evaluation parameters with the case summary as prepared by the lawyer for appeal in court. Further, suggestions for improving the proposed method are also presented.