PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles
Nikolaidis, Nikolaos Stefanovitch, Nicolas Silvano, Purificacao Dimitrov, Dimitar Iliyanov Yangarber, Roman Guimaraes, Nuno Sartori, Elisa Androutsopoulos, Ion Nakov, Preslav Da San Martino, Giovanni
Nikolaidis, Nikolaos
Stefanovitch, Nicolas
Silvano, Purificacao
Dimitrov, Dimitar Iliyanov
Yangarber, Roman
Guimaraes, Nuno
Sartori, Elisa
Androutsopoulos, Ion
Nakov, Preslav
Da San Martino, Giovanni
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Department
Natural Language Processing
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Conference proceeding
Date
2025
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English
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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.
Citation
N. Nikolaidis et al., “PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles,” 2025. [Online]. Available: https://aclanthology.org/2025.acl-long.1513/
Source
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics
Conference
63rd Annual Meeting of the Association for Computational Linguistics, 2025
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63rd Annual Meeting of the Association for Computational Linguistics, 2025
Publisher
Association for Computational Linguistics