@inproceedings{guimaraes-etal-2025-narratex,
    title = "{N}arrat{EX} Dataset: Explaining the Dominant Narratives in News Texts",
    author = "Guimar{\~a}es, Nuno  and
      Silvano, Purifica{\c{c}}{\~a}o  and
      Campos, Ricardo  and
      Jorge, Alipio  and
      Pacheco, Ana Filipa  and
      Dimitrov, Dimitar Iliyanov  and
      Nikolaidis, Nikolaos  and
      Yangarber, Roman  and
      Sartori, Elisa  and
      Stefanovitch, Nicolas  and
      Nakov, Preslav  and
      Piskorski, Jakub  and
      Da San Martino, Giovanni",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1112/",
    doi = "10.18653/v1/2025.findings-emnlp.1112",
    pages = "20408--20434",
    ISBN = "979-8-89176-335-7",
    abstract = "We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse."
}