Robust Deep Reinforcement Learning for Extractive Legal Summarization
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.
Cite
@article{arxiv.2111.07158,
title = {Robust Deep Reinforcement Learning for Extractive Legal Summarization},
author = {Duy-Hung Nguyen and Bao-Sinh Nguyen and Nguyen Viet Dung Nghiem and Dung Tien Le and Mim Amina Khatun and Minh-Tien Nguyen and Hung Le},
journal= {arXiv preprint arXiv:2111.07158},
year = {2022}
}