A Deep Learning-Based System for Automatic Case Summarization

TL;DR

This paper introduces a deep learning system that automatically summarizes lengthy legal case documents, combining supervised and unsupervised NLP techniques to improve legal analysis efficiency.

Contribution

It presents a novel deep learning-based system with a user-friendly interface for automatic legal case summarization, integrating multiple NLP methods.

Findings

System generates concise summaries for legal documents

Improves efficiency in legal case analysis

Supports both supervised and unsupervised summarization methods

Abstract

This paper presents a deep learning-based system for efficient automatic case summarization. Leveraging state-of-the-art natural language processing techniques, the system offers both supervised and unsupervised methods to generate concise and relevant summaries of lengthy legal case documents. The user-friendly interface allows users to browse the system's database of legal case documents, select their desired case, and choose their preferred summarization method. The system generates comprehensive summaries for each subsection of the legal text as well as an overall summary. This demo streamlines legal case document analysis, potentially benefiting legal professionals by reducing workload and increasing efficiency. Future work will focus on refining summarization techniques and exploring the application of our methods to other types of legal texts.