@inproceedings{ravaut-etal-2023-unsupervised,
    title = "Unsupervised Summarization Re-ranking",
    author = "Ravaut, Mathieu  and
      Joty, Shafiq  and
      Chen, Nancy",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.529",
    doi = "10.18653/v1/2023.findings-acl.529",
    pages = "8341--8376",
    abstract = "With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised models still lags significantly behind their supervised counterparts. Similarly to the supervised setup, we notice a very high variance in quality among summary candidates from these models while only one candidate is kept as the summary output. In this paper, we propose to re-rank summary candidates in an unsupervised manner, aiming to close the performance gap between unsupervised and supervised models. Our approach improves the unsupervised PEGASUS by up to 7.27{\%} and ChatGPT by up to 6.86{\%} relative mean ROUGE across four widely-adopted summarization benchmarks ; and achieves relative gains of 7.51{\%} (up to 23.73{\%} from XSum to WikiHow) averaged over 30 zero-shot transfer setups (finetuning on a dataset, evaluating on another).",
}

With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised models still lags significantly behind their supervised counterparts. Similarly to the supervised setup, we notice a very high variance in quality among summary candidates from these models while only one candidate is kept as the summary output. In this paper, we propose to re-rank summary candidates in an unsupervised manner, aiming to close the performance gap between unsupervised and supervised models. Our approach improves the unsupervised PEGASUS by up to 7.27{\%} and ChatGPT by up to 6.86{\%} relative mean ROUGE across four widely-adopted summarization benchmarks ; and achieves relative gains of 7.51{\%} (up to 23.73{\%} from XSum to WikiHow) averaged over 30 zero-shot transfer setups (finetuning on a dataset, evaluating on another).
Ravaut, Mathieu Joty, Shafiq Chen, Nancy
Unsupervised Summarization Re-ranking