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.

本文提出了一种基于深度学习的有效自动案例总结系统，该系统利用最先进的自然语言处理技术，提供了监督和无监督方法来生成简洁且相关的冗长法律案例文件的摘要。用户友好的界面允许用户浏览系统的法律案例库，选择所需案例，并选择他们偏好的总结方法。该系统为法律文本的每个小节以及整体生成全面的摘要。该演示简化了法律案例文档分析，可能通过减轻工作量和提高效率从而使法律专业人员受益。未来的工作将集中在改进摘要技术并探索将我们的方法应用于其他类型的法律文本。