The manual analysis of legal instruments—ranging from binding contracts and complex agreements to judicial precedents—is often impeded by their intricate syntax and voluminous nature. This research presents an intelligent, automated summarization framework designed to distill lengthy legal texts into concise, actionable summaries without compromising semantic integrity. The proposed system employs a multi-layered Natural Language Processing (NLP) pipeline, incorporating rigorous preprocessing phases such as lemmatization and tokenization alongside domain-specific cleaning. Distinguishing itself from traditional tools, this architecture utilizes a hybrid methodology that integrates graph-based ranking (TextRank/LexRank) for extractive precision with Transformer-based models (BART/Legal-BERT) for abstractive coherence. Furthermore, the system incorporates a conversational interpretation module powered by Retrieval-Augmented Generation (RAG) to allow interactive clause clarification. Initial findings suggest that this dual-model approach significantly enhances document accessibility and professional efficiency, bridging the gap between complex legal terminology and user comprehension.
N et al. (Thu,) studied this question.
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