Effective Summarization Techniques for Legal Document Analysis

Summarizing legal texts is a critical task in natural language processing (NLP), which addresses the growing demands for efficient processing of large legal documents. Due to the complexity and structured nature of legal texts, the development of models capable of generating accurate and concise summaries presents significant challenges. This study examines the use of three state-of-the-art transformer-based models for summarizing legal texts. Each model is evaluated for its ability to capture nuanced semantics and preserve the contextual integrity of legal documents. The research uses a specialized dataset containing different types of legal documents, including summaries of case law, statutes and regulatory texts. Preprocessing steps include tokenization, normalization, and removal of non-informative content, ensuring that model inputs reflect the structure and verbosity contained in legal texts. Performance benchmarking is done using standard metrics such as ROUGE, BLEU, and BERT Score, along with domain-specific criteria to assess factual consistency and relevance. Experimental consequences exhibit that BART-base excels in generating fluent and human-like summaries, LED-base proves effective for longer documents due to its prolonged context-handling ability, and T5 showcases its versatility in domain edition. A specified analysis famous the strengths and limitations of every version in dealing with domain-unique demanding situations consisting of felony terminology, quotation references, and logical reasoning.