Factually Consistent Summarization via Reinforcement Learning with
  Textual Entailment Feedback
Paul Roit Johan Ferret Lior Shani Roee Aharoni Geoffrey Cideron Robert Dadashi Matthieu Geist Sertan Girgin Léonard Hussenot Orgad Keller Nikola Momchev Sabela Ramos Piotr Stanczyk Nino Vieillard Olivier Bachem Gal Elidan Avinatan Hassidim Olivier Pietquin Idan Szpektor
Despite the seeming success of contemporary grounded text generation systems,
they often tend to generate factually inconsistent text with respect to their
input. This phenomenon is emphasized in tasks like summarization, in which the
generated summaries should be corroborated by their source article. In this
work, we leverage recent progress on textual entailment models to directly
address this problem for abstractive summarization systems. We use
reinforcement learning with reference-free, textual entailment rewards to
optimize for factual consistency and explore the ensuing trade-offs, as
improved consistency may come at the cost of less informative or more
extractive summaries. Our results, according to both automatic metrics and
human evaluation, show that our method considerably improves the faithfulness,
salience, and conciseness of the generated summaries.