@inproceedings{kurita-etal-2023-tohokunlp,
    title = "{T}ohoku{NLP} at {S}em{E}val-2023 Task 5: Clickbait Spoiling via Simple {S}eq2{S}eq Generation and Ensembling",
    author = "Kurita, Hiroto  and
      Ito, Ikumi  and
      Funayama, Hiroaki  and
      Sasaki, Shota  and
      Moriya, Shoji  and
      Mengyu, Ye  and
      Kokuta, Kazuma  and
      Hatakeyama, Ryujin  and
      Sone, Shusaku  and
      Inui, Kentaro",
    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-1.243",
    doi = "10.18653/v1/2023.semeval-1.243",
    pages = "1756--1762",
    abstract = "This paper describes our system submitted to SemEval-2023 Task 5: Clickbait Spoiling. We work on spoiler generation of the subtask 2 and develop a system which comprises two parts: 1) simple seq2seq spoiler generation and 2) post-hoc model ensembling. Using this simple method, we address the challenge of generating multipart spoiler. In the test set, our submitted system outperformed the baseline by a large margin (approximately 10 points above on the BLEU score) for mixed types of spoilers. We also found that our system successfully handled the challenge of the multipart spoiler, confirming the effectiveness of our approach.",
}

This paper describes our system submitted to SemEval-2023 Task 5: Clickbait Spoiling. We work on spoiler generation of the subtask 2 and develop a system which comprises two parts: 1) simple seq2seq spoiler generation and 2) post-hoc model ensembling. Using this simple method, we address the challenge of generating multipart spoiler. In the test set, our submitted system outperformed the baseline by a large margin (approximately 10 points above on the BLEU score) for mixed types of spoilers. We also found that our system successfully handled the challenge of the multipart spoiler, confirming the effectiveness of our approach.
Kurita, Hiroto Ito, Ikumi Funayama, Hiroaki Sasaki, Shota Moriya, Shoji Mengyu, Ye Kokuta, Kazuma Hatakeyama, Ryujin Sone, Shusaku Inui, Kentaro
TohokuNLP at SemEval-2023 Task 5: Clickbait Spoiling via Simple Seq2Seq Generation and Ensembling