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.

翻译：本文提出了一种基于深度学习的自动案件摘要生成系统，能够高效处理法律案件摘要任务。该系统融合了最先进的自然语言处理技术，提供有监督与无监督两种方法，可生成冗长法律案件文书的精炼且相关的摘要。其用户友好型界面支持用户浏览案件文书数据库、选定目标案例并选择偏好的摘要生成方法。系统不仅能生成法律文本各章节的详细摘要，还可输出整体摘要。本演示系统简化了法律案件文书分析流程，有望通过减少工作量并提升效率为法律从业者提供助益。未来工作将聚焦于摘要技术的优化，并探索将本方法拓展应用于其他类型法律文本的可能性。