@inproceedings{stormer-etal-2023-sam,
    title = "{S}am Miller at {S}em{E}val-2023 Task 5: Classification and Type-specific Spoiler Extraction Using {XLNET} and Other Transformer Models",
    author = {St{\"o}rmer, Pia  and
      Esser, Tobias  and
      Thomasius, Patrick},
    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://preview.aclanthology.org/fix-sig-urls/2023.semeval-1.169/",
    doi = "10.18653/v1/2023.semeval-1.169",
    pages = "1217--1224",
    abstract = "This paper proposes an approach to classify andan approach to generate spoilers for clickbaitarticles and posts. For the spoiler classification,XLNET was trained to fine-tune a model. Withan accuracy of 0.66, 2 out of 3 spoilers arepredicted accurately. The spoiler generationapproach involves preprocessing the clickbaittext and post-processing the output to fit thespoiler type. The approach is evaluated on atest dataset of 1000 posts, with the best resultfor spoiler generation achieved by fine-tuninga RoBERTa Large model with a small learningrate and sample size, reaching a BLEU scoreof 0.311. The paper provides an overview ofthe models and techniques used and discussesthe experimental setup."
}