@inproceedings{sterz-etal-2023-ml,
    title = "{ML} Mob at {S}em{E}val-2023 Task 5: {``}Breaking News: Our Semi-Supervised and Multi-Task Learning Approach Spoils Clickbait{''}",
    author = "Sterz, Hannah  and
      Bongard, Leonard  and
      Werner, Tobias  and
      Poth, Clifton  and
      Hentschel, Martin",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.251",
    doi = "10.18653/v1/2023.semeval-1.251",
    pages = "1818--1823",
    abstract = "Online articles using striking headlines that promise intriguing information are often used to attract readers. Most of the time, the information provided in the text is disappointing to the reader after the headline promised exciting news. As part of the SemEval-2023 challenge, we propose a system to generate a spoiler for these headlines. The spoiler provides the information promised by the headline and eliminates the need to read the full article. We consider Multi-Task Learning and generating more data using a distillation approach in our system. With this, we achieve an F1 score up to 51.48{\%} on extracting the spoiler from the articles.",
}

Online articles using striking headlines that promise intriguing information are often used to attract readers. Most of the time, the information provided in the text is disappointing to the reader after the headline promised exciting news. As part of the SemEval-2023 challenge, we propose a system to generate a spoiler for these headlines. The spoiler provides the information promised by the headline and eliminates the need to read the full article. We consider Multi-Task Learning and generating more data using a distillation approach in our system. With this, we achieve an F1 score up to 51.48{\%} on extracting the spoiler from the articles.
Sterz, Hannah Bongard, Leonard Werner, Tobias Poth, Clifton Hentschel, Martin
ML Mob at SemEval-2023 Task 5: “Breaking News: Our Semi-Supervised and Multi-Task Learning Approach Spoils Clickbait”