A Rhetorical Role Relatedness (RRR) framework for Legal Case Brief Generation

Language is crucial for expressing thoughts, ideas, and feelings, but computers typically understand structured language, processing it to generate meaningful results. However, many fields, like the legal domain, use unstructured or semi-structured language, which lacks specific syntax and context-dependent semantics. Text summarization was once challenging, but with the advent and widespread use of Large Language Models (LLMs), it has become more efficient. Legal briefs are concise summaries of extensive judgments requiring legal decision-making. This research investigates legal text summarization, examining if current symbolic or non-symbolic AI models can effectively generate legal case briefs. The study uses the Rhetorical Roles (RRs) generated by the BUILDNyAI RR model and introduces a novel Rhetorical Role Relatedness (RRR) framework. This research had tried to augment the legal reasoning process that will help a legal profession to drafting legal briefs. This framework successfully creates legal briefs closely matching the golden standards using RR Similarity Scores and the LexRank summarizer, achieving higher ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L-Sum) compared to the BUILDNyAI Summarizer model