A Survey of Summarization Techniques of Legal Documents





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A Survey of Summarization Techniques of Legal Documents

Kate Kavita1

Guide- Prof. Dr.S.S.Bere

Student, Department of Computer Engineering, Dattakala Group of Institutions Swami-chincholi, Tal-Daund, Dist-Pune 413130, Maharashtra, India

Abstract - In the Indian judicial system, pending cases exceed 40 million, highlighting the need for efficient legal document summarization. Manually processing large volumes of legal text is labor-intensive and prone to errors, further complicating judicial workflows. Despite advancements in machine learning and natural language processing, existing text summarization models often underperform when applied to the specialized language of legal texts. This survey examines the challenges of legal document summarization and explores state-of-the-art techniques, including both extractive and abstractive methods. The findings emphasize the potential for tailored approaches to address the unique requirements of legal texts, enhancing the efficiency and accuracy of the summarization process.

Key Words: Abstractive summarization, Deep learning, Extractive summarization, Legal document summarization, Natural language processing, Text summarization techniques.

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