Machine Learning Powered System for Legal Document Summarization and Intelligent Law Assistance
DOI:
https://doi.org/10.64751/Keywords:
Machine Learning, Natural Language Processing (NLP), Legal Document Summarization, Text Mining, Artificial Intelligence, Legal Analytics, Information Extraction, Automated Legal Assistance, Text Classification, Knowledge Representation, Deep Learning, Legal Information Retrieval, Document Analysis, Intelligent Decision Support, Legal Technology.Abstract
The rapid growth of legal documentation in courts, law firms, corporate sectors, and
government institutions has made manual analysis time-consuming and complex. Legal
documents are often lengthy, technical, and difficult for common citizens to understand. The
proposed system, AI-Based Legal Document Summarizer & Law Advisor Using Machine
Learning, aims to develop an intelligent platform capable of automatically summarizing legal
documents and providing preliminary legal advice using advanced Machine Learning (ML)
and Natural Language Processing (NLP) techniques.The system leverages transformer-based
models, text classification algorithms, and semantic similarity analysis to generate concise
summaries and respond to legal queries. It reduces manual effort, improves efficiency,
enhances access to justice, and assists lawyers and clients in understanding complex legal
texts quickly. The solution is scalable, cost-effective, and capable of continuous improvement
through model training and feedback mechanisms.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
