@inproceedings{martinez-murillo-etal-2025-gplsicortex,
    title = "{GPLSICORTEX} at {S}em{E}val-2025 Task 10: Leveraging Intentions for Generating Narrative Extractions",
    author = "Martinez - Murillo, Ivan  and
      Mir{\'o} Maestre, Mar{\'i}a  and
      Mart{\'i}nez, Aitana  and
      Ralund, Snorre  and
      Lloret, Elena  and
      Moreda Pozo, Paloma  and
      Su{\'a}rez Cueto, Armando",
    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/corrections-2025-08/2025.semeval-1.80/",
    pages = "575--583",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes our approach to address the SemEval-2025 Task 10 subtask 3, which is focused on narrative extraction given news articles with a dominant narrative. We design an external knowledge injection approach to fine-tune a Flan-T5 model so the generated narrative explanations are in line with the dominant narrative determined in each text. We also incorporate pragmatic information in the form of communicative intentions, using them as external knowledge to assist the model. This ensures that the generated texts align more closely with the intended explanations and effectively convey the expected meaning. The results show that our approach ranks 3rd in the task leaderboard (0.7428 in Macro-F1) with concise and effective news explanations. The analyses highlight the importance of adding pragmatic information when training systems to generate adequate narrative extractions."
}