Robust Deep Reinforcement Learning for Extractive Legal Summarization

• URL: http://arxiv.org/abs/2111.07158v1
• Date: Sat, 13 Nov 2021 17:27:49 GMT
• Title: Robust Deep Reinforcement Learning for Extractive Legal Summarization
• Authors: Duy-Hung Nguyen, Bao-Sinh Nguyen, Nguyen Viet Dung Nghiem, Dung Tien Le, Mim Amina Khatun, Minh-Tien Nguyen, and Hung Le
• Abstract summary: We propose to use reinforcement learning to train current deep summarization models to improve their performance on the legal domain. We observe a consistent and significant performance gain across 3 public legal datasets.
• Score: 13.577343899514396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles. Recent advances of deep models trained end-to-end with differentiable losses can well-summarize natural text, yet when applied to legal domain, they show limited results. In this paper, we propose to use reinforcement learning to train current deep summarization models to improve their performance on the legal domain. To this end, we adopt proximal policy optimization methods and introduce novel reward functions that encourage the generation of candidate summaries satisfying both lexical and semantic criteria. We apply our method to training different summarization backbones and observe a consistent and significant performance gain across 3 public legal datasets.

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