  A commonly observed problem with the state-of-the art abstractive
summarization models is that the generated summaries can be factually
inconsistent with the input documents. The fact that automatic summarization
may produce plausible-sounding yet inaccurate summaries is a major concern that
limits its wide application. In this paper we present an approach to address
factual consistency in summarization. We first propose an efficient automatic
evaluation metric to measure factual consistency; next, we propose a novel
learning algorithm that maximizes the proposed metric during model training.
Through extensive experiments, we confirm that our method is effective in
improving factual consistency and even overall quality of the summaries, as
judged by both automatic metrics and human evaluation.
