International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | RL – Powered Legal Document Summarization |
|---|---|
| ABSTRACT | Legal documents such as court judgments, case reports, and judicial decisions are often lengthy and complex, making manual analysis time-consuming and inefficient. To address this challenge, the proposed RL Powered Legal Document Summarization System employs the BART (Bidirectional and Auto-Regressive Transformer) model to generate abstractive summaries of legal documents. After preprocessing and tokenization, the system produces one-line, two-line, and detailed summaries by capturing contextual relationships within legal text. To further enhance summary quality, multiple candidate summaries are generated using beam search decoding and evaluated using the ROUGE-L metric, which acts as a reward function. The summary with the highest reward score is selected as the final optimized summary, thereby improving relevance, accuracy, and readability while preserving the essential information contained in the original legal judgment. |
| AUTHOR | TEJASVI ALUVALA Post Graduate Student, M. Tech Data Science, Department. of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406067 |
| pdf/67_RL – Powered Legal Document Summarization.pdf | |
| KEYWORDS | |
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