@inproceedings{choi-na-2025-kyuhyunchoi,
    title = "{K}yu{H}yun{C}hoi at {S}em{E}val-2025 Task 10: Narrative Extraction Using a Summarization-Specific Pretrained Model",
    author = "Choi, Kyu Hyun  and
      Na, Seung Hoon",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/more-markup/2025.semeval-1.294/",
    pages = "2262--2264",
    ISBN = "979-8-89176-273-2",
    abstract = "Task 11 of SemEval 2025 was proposed to develop supporting information for analyzing the risks of misinformation and propaganda in news articles. In this study, we selected Sub-task 3{---}which involves generating evidence explaining why a particular dominant narrative is labeled in an article{---}and fine-tuned PEGASUS for this purpose, achieving the best performance in the competition."
}