A Deep Learning-Based System for Automatic Case Summarization
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Abstract (arXiv)
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.
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References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2020What Have We Achieved on Text Summarization?cited by 65
- 2022Attentive Deep Neural Networks for Legal Document Retrievalcited by 13
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