@inproceedings{nikolaidis-etal-2025-polynarrative,
    title = "{P}oly{N}arrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles",
    author = "Nikolaidis, Nikolaos  and
      Stefanovitch, Nicolas  and
      Silvano, Purifica{\c{c}}{\~a}o  and
      Dimitrov, Dimitar Iliyanov  and
      Yangarber, Roman  and
      Guimar{\~a}es, Nuno  and
      Sartori, Elisa  and
      Androutsopoulos, Ion  and
      Nakov, Preslav  and
      Da San Martino, Giovanni  and
      Piskorski, Jakub",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1513/",
    pages = "31323--31345",
    ISBN = "979-8-89176-251-0",
    abstract = "We present polyNarrative, a new multilingual dataset of news articles, annotated for narratives. Narratives are overt or implicit claims, recurring across articles and languages, promoting a specific interpretation or viewpoint on an ongoing topic, often propagating mis/disinformation. We developed two-level taxonomies with coarse- and fine-grained narrative labels for two domains: (i) climate change and (ii) the military conflict between Ukraine and Russia. We collected news articles in four languages (Bulgarian, English, Portuguese, and Russian) related to the two domains and manually annotated them at the paragraph level. We make the dataset publicly available, along with experimental results of several strong baselines that assign narrative labels to news articles at the paragraph or the document level. We believe that this dataset will foster research in narrative detection and enable new research directions towards more multi-domain and highly granular narrative related tasks."
}