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.
|Original language||American English|
|Title of host publication||Long Papers|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||21|
|State||Published - 2023|
|Event||61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada|
Duration: 9 Jul 2023 → 14 Jul 2023
|Name||Proceedings of the Annual Meeting of the Association for Computational Linguistics|
|Conference||61st Annual Meeting of the Association for Computational Linguistics, ACL 2023|
|Period||9/07/23 → 14/07/23|
Bibliographical notePublisher Copyright:
© 2023 Association for Computational Linguistics.