The number of legal documents is growing so fast and this has created a very big need for ways to analyze the legal text that are automatic which can handle a lot of work and get better understanding of the text.The growth of legal databases has created this demand that motivated us to represent this paper JURISYNTH, an intelligence framework that used advanced Natural Language Processing techniques to make the legal documents simpler and easier to summarize and predict legal outcomes, contractual risks with high accuracy and case precedents.A hybrid pipeline integrating lexical simplification and syntactic normalization employed by a proposed system.This system uses a combination of techniques to simplify language make the structure of sentences clear and summarize the documents in a way that combines extracting essential information and generating a summary.This is made possible by models like LEGAL-BERT and LEGAL-PEGASUS.To ensure the summaries are accurate and are based on the context; the system uses Retrieval-Augmented Generation to search for information using vectors which enhances the response of the system.This system also has a module to represent entities in a structured way and a layer that allows human beings to check the results to prevent errors and ensure that the system is reliable especially, in important legal cases.Instead, getting overwhelmed by a 60 -page contract, we get a simple breakdown of what is fair, what is risky and what the fine print actually means.JURISYNTH is a system that provides an easy way to understand and efficiently handle the confidential legal texts and documents to support legal decisions in a reliable way.
Paper
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