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A Deep Learning-Based System for Automatic Case Summarization (2312.07824v1)

Published 13 Dec 2023 in cs.CL

Abstract: 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 (19)
  1. A knowledge representation for Vietnamese legal document system. In: 2017 9th international conference on knowledge and systems engineering (kse). IEEE; 2017. p. 30-5.
  2. What have we achieved on text summarization? arXiv preprint arXiv:201004529. 2020.
  3. Casesummarizer: A system for automated summarization of legal texts. In: Proceedings of COLING 2016, the 26th international conference on Computational Linguistics: System Demonstrations; 2016. p. 258-62.
  4. Automatic summarization of legal decisions using iterative masking of predictive sentences. In: Proceedings of the seventeenth international conference on artificial intelligence and law; 2019. p. 163-72.
  5. Farzindar A, Lapalme G. Legal text summarization by exploration of the thematic structure and argumentative roles. In: Text Summarization Branches Out; 2004. p. 27-34.
  6. Mihalcea R, Tarau P. Textrank: Bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language processing; 2004. p. 404-11.
  7. Erkan G, Radev DR. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research. 2004;22:457-79.
  8. Jing H. Sentence reduction for automatic text summarization. In: Sixth applied natural language processing conference; 2000. p. 310-5.
  9. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805. 2018.
  10. Language models are unsupervised multitask learners. OpenAI blog. 2019;1(8):9.
  11. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:191013461. 2019.
  12. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research. 2020;21(1):5485-551.
  13. Liu Y, Lapata M. Text summarization with pretrained encoders. arXiv preprint arXiv:190808345. 2019.
  14. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In: International Conference on Machine Learning. PMLR; 2020. p. 11328-39.
  15. Improving legal document summarization using graphical models. Frontiers in Artificial Intelligence and Applications. 2006;152:51.
  16. Feijo DdV, Moreira VP. Improving abstractive summarization of legal rulings through textual entailment. Artificial intelligence and law. 2023;31(1):91-113.
  17. SM-BERT-CR: a deep learning approach for case law retrieval with supporting model. Artificial Intelligence and Law. 2022:1-28.
  18. Attentive deep neural networks for legal document retrieval. Artificial Intelligence and Law. 2022:1-30.
  19. Answering legal questions by learning neural attentive text representation. In: Proceedings of the 28th International Conference on Computational Linguistics; 2020. p. 988-98.
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Authors (5)
  1. Minh Duong (1 paper)
  2. Long Nguyen (20 papers)
  3. Yen Vuong (1 paper)
  4. Trong Le (1 paper)
  5. Ha-Thanh Nguyen (33 papers)