@inproceedings{pan-etal-2023-chick,
    title = "Chick Adams at {S}em{E}val-2023 Task 5: Using {R}o{BERT}a and {D}e{BERT}a to Extract Post and Document-based Features for Clickbait Spoiling",
    author = "Pan, Ronghao  and
      Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio  and
      Garc{\'\i}a-S{\'a}nchez, Franciso  and
      Valencia-Garc{\'\i}a, Rafael",
    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-1.85",
    doi = "10.18653/v1/2023.semeval-1.85",
    pages = "624--628",
    abstract = "In this manuscript, we describe the participation of the UMUTeam in SemEval-2023 Task 5, namely, Clickbait Spoiling, a shared task on identifying spoiler type (i.e., a phrase or a passage) and generating short texts that satisfy curiosity induced by a clickbait post, i.e. generating spoilers for the clickbait post. Our participation in Task 1 is based on fine-tuning pre-trained models, which consists in taking a pre-trained model and tuning it to fit the spoiler classification task. Our system has obtained excellent results in Task 1: we outperformed all proposed baselines, being within the Top 10 for most measures. Foremost, we reached Top 3 in F1 score in the passage spoiler ranking.",
}

In this manuscript, we describe the participation of the UMUTeam in SemEval-2023 Task 5, namely, Clickbait Spoiling, a shared task on identifying spoiler type (i.e., a phrase or a passage) and generating short texts that satisfy curiosity induced by a clickbait post, i.e. generating spoilers for the clickbait post. Our participation in Task 1 is based on fine-tuning pre-trained models, which consists in taking a pre-trained model and tuning it to fit the spoiler classification task. Our system has obtained excellent results in Task 1: we outperformed all proposed baselines, being within the Top 10 for most measures. Foremost, we reached Top 3 in F1 score in the passage spoiler ranking.
Pan, Ronghao Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio Garc{\'\i}a-S{\'a}nchez, Franciso Valencia-Garc{\'\i}a, Rafael
Chick Adams at SemEval-2023 Task 5: Using RoBERTa and DeBERTa to Extract Post and Document-based Features for Clickbait Spoiling