A Deep Learning-Based System for Automatic Case Summarization
This is an incremental improvement for legal professionals by automating case summarization to streamline document analysis.
The paper tackled the problem of summarizing lengthy legal case documents by developing a deep learning-based system that uses supervised and unsupervised methods to generate concise and relevant summaries, including for subsections and overall cases, which could reduce workload and increase efficiency for legal professionals.
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.