Legal document summarizer

This paper introduces an AI-based tool for summarization that leverages the generations of legal and technical knowledge embedded in long, complicated legal documents. It builds on the Bidirectional and Auto-Regressive Transformers (BART) model, an advanced sequencing-to-sequencing architecture that can deliver comprehensible and condensed summaries while preserving critical legal content such as facts, issues, and decisions. The tool is fine-tuned on legal-specific datasets by training the BART model on legal texts which makes it suitable for the unique structure and language of legal texts. This is useful to save manual review things by making productivity increase for legal professionals, researchers and other end users. This project is a practical implementation of cutting-edge NLP for the legal application domain, offering a tool that increases access to information, aids in informed decision-making and makes the tasks of legal research and case preparation easier. The ROUGE scores express how well the system works, certainly in turn makes the system very effective and reliable in the real-world use case.