Contract Sentinel: A Literature Survey on Transformer-Based Clause Extraction and Risk Detection in Legal Contracts

Abstract: Legal contracts such as non-disclosure agreements, lease agreements, and employment contracts often contain complex clauses that can create substantial financial, legal, and operational risk. Manually reviewing these documents takes time and requires domain expertise that individuals, startups, or small businesses do not always have, leaving them vulnerable to missing unfavourable or unusual terms. In this project, we present Contract Sentinel, an AI system that automatically reviews legal contracts and flags risky or unusual provisions for manual review. The system is based on natural language processing and machine learning algorithms trained on the CUAD (Contract Understanding Atticus Dataset), consisting of 510 real-world contracts with more than 13,000 expert labels across 41 clause types. The system takes a contract document as input, extracts clauses from it, and assigns each clause to a known risk category such as indemnification, liability caps, termination rights, non-compete terms, and auto-renewal conditions. Unusual or unfair clauses are marked with a description of the danger in plain English, allowing users to rapidly grasp and negotiate complex agreements without the need for legal expertise. Contract Sentinel uses transformer-based language models and a curated legal clause taxonomy to speed up and improve contract review, making it more accessible and reliable by reducing the need for expensive legal consultation for routine document checks.

Keywords: Legal NLP, Contract Review, Clause Classification, Risk Detection, CUAD, Legal-BERT, Transformers, Explainable AI