• Rudrendra Bahadur Singh
• Shobhit Sinha
• Ankita Singh

Legal documents can be challenging for both laypeople and experts to comprehend due to their complex structure and technical jargon. These difficulties include figuring out crucial clauses, comprehending legalese, and determining whether regulations are being followed. These issues are addressed by the AI-powered platform BrieflyLegal using deep learning models and sophisticated natural language processing (NLP) methods. The T5 model is a customized version of the Text-To-Text Transfer Transformer (T5) intended for summarizing legal documents. To improve its comprehension of domain-specific vocabulary and intricate legal structures, T5-L is pretrained on extensive legal corpora, such as statutes, contracts, and case law, utilizing the fundamental architecture of T5. To enhance performance on tasks including extractive and abstractive summarization, legal clause categorization, and risk assessment, this model is refined utilizing datasets like as the Stanford Question Answering Dataset (SQUAD) and the Contract Understanding Atticus Dataset (CUAD). T5-L reduces redundancy while maintaining crucial legal information by using task-adaptive pretraining and knowledge graph-based fine-tuning to guarantee contextually correct and succinct summaries. The software ensures real-time compliance monitoring while automating critical tasks like sentiment analysis, paraphrasing, and clause identification.

"Bridging AI and Law: A Comprehensive NLP Platform for Legal Document Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.c700-c706, December-2025, Available :http://www.jetir.org/papers/JETIR2512282.pdf

"Bridging AI and Law: A Comprehensive NLP Platform for Legal Document Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppc700-c706, December-2025, Available at : http://www.jetir.org/papers/JETIR2512282.pdf