  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.

Computation and Language;Artificial Intelligence