@inproceedings{3fd1c0fd170f4ac9bc7565115a5262ac,
title = "Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback",
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 tradeoffs, 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.",
author = "Paul Roit and Johan Ferret and Lior Shani and Roee Aharoni and Geoffrey Cideron and Robert Dadashi and Matthieu Geist and Sertan Girgin and L{\'e}onard Hussenot and Orgad Keller and Nikola Momchev and Sabela Ramos and Piotr Stanczyk and Nino Vieillard and Olivier Bachem and Gal Elidan and Avinatan Hassidim and Olivier Pietquin and Idan Szpektor",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
doi = "https://doi.org/10.18653/v1/2023.acl-long.344",
language = "الإنجليزيّة",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "6252--6272",
booktitle = "Long Papers",
address = "الولايات المتّحدة",
}