@inproceedings{hagen-etal-2022-clickbait,
    title = "Clickbait Spoiling via Question Answering and Passage Retrieval",
    author = {Hagen, Matthias  and
      Fr{\"o}be, Maik  and
      Jurk, Artur  and
      Potthast, Martin},
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.acl-long.484/",
    doi = "10.18653/v1/2022.acl-long.484",
    pages = "7025--7036",
    abstract = "We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertises its contents by arousing curiosity instead of providing an informative summary. Our contributions are approaches to classify the type of spoiler needed (i.e., a phrase or a passage), and to generate appropriate spoilers. A large-scale evaluation and error analysis on a new corpus of 5,000 manually spoiled clickbait posts{---}the Webis Clickbait Spoiling Corpus 2022{---}shows that our spoiler type classifier achieves an accuracy of 80{\%}, while the question answering model DeBERTa-large outperforms all others in generating spoilers for both types."
}