Neural abstractive summarization models are able to generate summaries which
have high overlap with human references. However, existing models are not
optimized for factual correctness, a critical metric in real-world
applications. In this work, we develop a general framework where we evaluate
the factual correctness of a generated summary by fact-checking it
automatically against its reference using an information extraction module. We
further propose a training strategy which optimizes a neural summarization
model with a factual correctness reward via reinforcement learning. We apply
the proposed method to the summarization of radiology reports, where factual
correctness is a key requirement. On two separate datasets collected from
hospitals, we show via both automatic and human evaluation that the proposed
approach substantially improves the factual correctness and overall quality of
outputs over a competitive neural summarization system, producing radiology
summaries that approach the quality of human-authored ones.

该研究提出了一个基于强化学习的训练策略，通过信息提取模块评估生成摘要的事实正确性，以优化称述的正确性。在将该策略应用于放射学摘要中时，得到的摘要输出在事实正确性和整体质量方面均优于竞争的神经网络摘要系统。