@inproceedings{piper-bagga-2025-narradetect,
    title = "{N}arra{D}etect: An annotated dataset for the task of narrative detection",
    author = "Piper, Andrew  and
      Bagga, Sunyam",
    editor = "Clark, Elizabeth  and
      Lal, Yash Kumar  and
      Chaturvedi, Snigdha  and
      Iyyer, Mohit  and
      Brei, Anneliese  and
      Modi, Ashutosh  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the The 7th Workshop on Narrative Understanding",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.wnu-1.1/",
    pages = "1--7",
    ISBN = "979-8-89176-247-3",
    abstract = "Narrative detection is an important task across diverse research domains where storytelling serves as a key mechanism for explaining human beliefs and behavior. However, the task faces three significant challenges: (1) inter-narrative heterogeneity, or the variation in narrative communication across social contexts; (2) intra-narrative heterogeneity, or the dynamic variation of narrative features within a single text over time; and (3) the lack of theoretical consensus regarding the concept of narrative. This paper introduces the NarraDetect dataset, a comprehensive resource comprising over 13,000 passages from 18 distinct narrative and non-narrative genres. Through a manually annotated subset of {\textasciitilde}400 passages, we also introduce a novel theoretical framework for annotating for a scalar concept of {\textquotedblleft}narrativity.{\textquotedblright} Our findings indicate that while supervised models outperform large language models (LLMs) on this dataset, LLMs exhibit stronger generalization and alignment with the scalar concept of narrativity."
}