@inproceedings{bao-etal-2022-suenes,
    title = "{S}ue{N}es: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling",
    author = "Bao, Forrest  and
      Luo, Ge  and
      Li, Hebi  and
      Qiu, Minghui  and
      Yang, Yinfei  and
      He, Youbiao  and
      Chen, Cen",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.175",
    doi = "10.18653/v1/2022.naacl-main.175",
    pages = "2450--2458",
    abstract = "Canonical automatic summary evaluation metrics, such as ROUGE, focus on lexical similarity which cannot well capture semantics nor linguistic quality and require a reference summary which is costly to obtain. Recently, there have been a growing number of efforts to alleviate either or both of the two drawbacks. In this paper, we present a proof-of-concept study to a weakly supervised summary evaluation approach without the presence of reference summaries. Massive data in existing summarization datasets are transformed for training by pairing documents with corrupted reference summaries. In cross-domain tests, our strategy outperforms baselines with promising improvements, and show a great advantage in gauging linguistic qualities over all metrics.",
}