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Integrating Extractive Techniques and Classification Methods for Legal Document Summarization
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Author(s): Alok Kumar (Faculty of Computing & Informatics, Sir Padampat Singhania University, Udaipur, India), Utsav Upadhyay (Faculty of Computing & Informatics, Sir Padampat Singhania University, Udaipur, India), Gajanand Sharma (Department of Computer Science and Engineering, JECRC University, Jaipur, India), Varsha Arya (Hong Kong Metropolitan University, Hong Kong, & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India & UCRD, Chandigarh University, Chandigarh, India), Wadee Alhalabi (Immersive Virtual Reality Research Group, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia), Bassma Saleh Alsulami (Faculty of computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)and Brij B. Gupta (Asia University, Taiwan)
Copyright: 2025
Volume: 21
Issue: 1
Pages: 21
Source title:
International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/IJDWM.379719
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Abstract
This article introduces an innovative text summarization mechanism designed to tackle the inherent challenges of condensing lengthy and unstructured legal documents in the context of India. The authors' primary aim is to create a system proficient in extracting crucial information from these documents, producing concise summaries akin to those crafted by humans. The proposed methodology frames summarization as a binary classification problem, employing an extractive summarization technique rooted in statistical features and word vectors. The system strategically identifies summary statements from the comprehensive input text section. To automate the summarization process, they leverage various classifiers, including logistic regression, gradient boosting, and neural networks. Through this multifaceted approach, they endeavor to enhance the efficiency and accuracy of legal document summarization, addressing a critical need in the field.
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