Computer Science > Computation and Language
arXiv:2306.00186 (cs)
[Submitted on 31 May 2023]
Title:Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Authors: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
View a PDF of the paper titled Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback, by Paul Roit and 17 other authors
View PDFAbstract: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.
| Comments: | ACL 2023 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2306.00186 [cs.CL] |
| (orarXiv:2306.00186v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2306.00186 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback, by Paul Roit and 17 other authors
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