A Deep Learning-Based System for Automatic Case Summarization
Minh Duong Long Nguyen Yen Vuong Trong Le Ha-Thanh Nguyen
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.