@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}.",
}

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}.
Chern, I-chun Wang, Zhiruo Das, Sanjan Sharma, Bhavuk Liu, Pengfei Neubig, Graham
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning