@inproceedings{chern-etal-2023-improving,
title = "Improving Factuality of Abstractive Summarization via Contrastive Reward Learning",
author = "Chern, I-chun and
Wang, Zhiruo and
Das, Sanjan and
Sharma, Bhavuk and
Liu, Pengfei and
Neubig, Graham",
editor = "Ovalle, Anaelia and
Chang, Kai-Wei and
Mehrabi, Ninareh and
Pruksachatkun, Yada and
Galystan, Aram and
Dhamala, Jwala and
Verma, Apurv and
Cao, Trista and
Kumar, Anoop and
Gupta, Rahul",
booktitle = "Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.trustnlp-1.6",
doi = "10.18653/v1/2023.trustnlp-1.6",
pages = "55--60",
abstract = "Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries. Code and human evaluation results will be publicly available at {\textbackslash}url{https://github.com/EthanC111/factuality{\_}summarization}.",
}
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<abstract>Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries. Code and human evaluation results will be publicly available at \textbackslashurlhttps://github.com/EthanC111/factuality_summarization.</abstract>
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%0 Conference Proceedings
%T Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
%A Chern, I-chun
%A Wang, Zhiruo
%A Das, Sanjan
%A Sharma, Bhavuk
%A Liu, Pengfei
%A Neubig, Graham
%Y Ovalle, Anaelia
%Y Chang, Kai-Wei
%Y Mehrabi, Ninareh
%Y Pruksachatkun, Yada
%Y Galystan, Aram
%Y Dhamala, Jwala
%Y Verma, Apurv
%Y Cao, Trista
%Y Kumar, Anoop
%Y Gupta, Rahul
%S Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F chern-etal-2023-improving
%X Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries. Code and human evaluation results will be publicly available at \textbackslashurlhttps://github.com/EthanC111/factuality_summarization.
%R 10.18653/v1/2023.trustnlp-1.6
%U https://aclanthology.org/2023.trustnlp-1.6
%U https://doi.org/10.18653/v1/2023.trustnlp-1.6
%P 55-60
Markdown (Informal)
[Improving Factuality of Abstractive Summarization via Contrastive Reward Learning](https://aclanthology.org/2023.trustnlp-1.6) (Chern et al., TrustNLP 2023)
ACL