Legal lens: Exploring NLP for Document Analysis in Law

This study explores the utilization of Natural Language Processing (NLP) methods for analyzing documents in the legal field. More specifically, the study concentrates on summarizing legal documents and comparing their content. By employing advanced NLP techniques, such as transformer-based models like BERT, GPT-2, the study aims to extract significant information from legal texts and evaluate the similarity between various documents. Through utilizing pre-training on specialized corpora and fine-tuning for legal contexts, the study seeks to enhance the performance of NLP models for analyzing legal documents. The results of this research effort show potential for increasing efficiency and accuracy while processing legal papers, thereby aiding in better decision-making and understanding of legal matters.