@inproceedings{roit-etal-2023-factually,
    title = "Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback",
    author = "Roit, Paul  and
      Ferret, Johan  and
      Shani, Lior  and
      Aharoni, Roee  and
      Cideron, Geoffrey  and
      Dadashi, Robert  and
      Geist, Matthieu  and
      Girgin, Sertan  and
      Hussenot, Leonard  and
      Keller, Orgad  and
      Momchev, Nikola  and
      Ramos Garea, Sabela  and
      Stanczyk, Piotr  and
      Vieillard, Nino  and
      Bachem, Olivier  and
      Elidan, Gal  and
      Hassidim, Avinatan  and
      Pietquin, Olivier  and
      Szpektor, Idan",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.344",
    doi = "10.18653/v1/2023.acl-long.344",
    pages = "6252--6272",
    abstract = "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.",
}

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.
Roit, Paul Ferret, Johan Shani, Lior Aharoni, Roee Cideron, Geoffrey Dadashi, Robert Geist, Matthieu Girgin, Sertan Hussenot, Leonard Keller, Orgad Momchev, Nikola Ramos Garea, Sabela Stanczyk, Piotr Vieillard, Nino Bachem, Olivier Elidan, Gal Hassidim, Avinatan Pietquin, Olivier Szpektor, Idan
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback