K yu H yun C hoi at S em E val-2025 Task 10: Narrative Extraction Using a Summarization-Specific Pretrained Model (2025)

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.

## introduction task 10 of semeval 2025 @xcite was proposed to support the research and development of new analytical functions aimed at analyzing the news ecosystem and characterizing manipulation attempts, in recognition that internet consumers are at risk of exposure to deceptive content and manipulation attempts, and that major crisis situations are also susceptible to the spread of harmful misinformation and propaganda. the task focused on climate change and the ukraine-russia conflict as its main topics and provided three subtasks related to news articles. subtask 1 involves assigning roles to each named entity mention in a news article using a predefined fine-grained role classification scheme. subtask 2 requires assigning all appropriate subordinate narrative labels to a given article based on a two-stage narrative labeling system specific to a domain. subtask 3 entails generating a free-text explanation, limited to 80 words, that provides evidence supporting the selection of the dominant narrative in an article. in this study, we chose subtask 3 and selected pegasus large @xcite as the model best suited for this task. we fine-tuned pegasus large using the provided training dataset for this challenge. section 2 explains the dataset and task for sub-task 3, section 3 describes the model used and fine-tuning process, section 4 details the hyperparameter settings and evaluation methods, section 5 compares the results from pegasus large with the baseline, and section 6 concludes. ## conclusion although it has been five years since the introduction of pegasus, our experiments have confirmed that it continues to exhibit robust performance, and the results of this study may offer valuable insights to the organizers of semeval task 11. while the three subtasks of task 11 have distinct characteristics, the third subtask can be effectively addressed by employing a summarization-specialized model such as pegasus.