Legal Document Summarization Using NLP

Abstract - Legal documents tend to be lengthy and complex, with it becoming difficult for legal experts and clients to pull out valuable information efficiently. This paper suggests an automatic summary system for legal documents based on Natural Language Processing (NLP). The system uses word embeddings, sentence vectorization, similarity measures, and a graph-based ranking technique to produce short yet accurate summaries. It allows for the reduction of time taken on thoroughly going through lengthy documents, without missing out any of the crucial information. The paper demonstrates the effectiveness of TextRank-based summarization in legal settings and sketches future improvements in order to enhance its accuracy as well as performance.