<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="piper-bagga-2025-narradetect">
    <titleInfo>
        <title>NarraDetect: An annotated dataset for the task of narrative detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Andrew</namePart>
        <namePart type="family">Piper</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sunyam</namePart>
        <namePart type="family">Bagga</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the The 7th Workshop on Narrative Understanding</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Elizabeth</namePart>
            <namePart type="family">Clark</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yash</namePart>
            <namePart type="given">Kumar</namePart>
            <namePart type="family">Lal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Snigdha</namePart>
            <namePart type="family">Chaturvedi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Iyyer</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anneliese</namePart>
            <namePart type="family">Brei</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ashutosh</namePart>
            <namePart type="family">Modi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Khyathi</namePart>
            <namePart type="given">Raghavi</namePart>
            <namePart type="family">Chandu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Albuquerque, New Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-247-3</identifier>
    </relatedItem>
    <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 ~400 passages, we also introduce a novel theoretical framework for annotating for a scalar concept of “narrativity.” 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.</abstract>
    <identifier type="citekey">piper-bagga-2025-narradetect</identifier>
    <identifier type="doi">10.18653/v1/2025.wnu-1.1</identifier>
    <location>
        <url>https://aclanthology.org/2025.wnu-1.1/</url>
    </location>
    <part>
        <date>2025-05</date>
        <extent unit="page">
            <start>1</start>
            <end>7</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T NarraDetect: An annotated dataset for the task of narrative detection
%A Piper, Andrew
%A Bagga, Sunyam
%Y Clark, Elizabeth
%Y Lal, Yash Kumar
%Y Chaturvedi, Snigdha
%Y Iyyer, Mohit
%Y Brei, Anneliese
%Y Modi, Ashutosh
%Y Chandu, Khyathi Raghavi
%S Proceedings of the The 7th Workshop on Narrative Understanding
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-247-3
%F piper-bagga-2025-narradetect
%X 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 ~400 passages, we also introduce a novel theoretical framework for annotating for a scalar concept of “narrativity.” 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.
%R 10.18653/v1/2025.wnu-1.1
%U https://aclanthology.org/2025.wnu-1.1/
%U https://doi.org/10.18653/v1/2025.wnu-1.1
%P 1-7

Markdown (Informal)

[NarraDetect: An annotated dataset for the task of narrative detection](https://aclanthology.org/2025.wnu-1.1/) (Piper & Bagga, WNU 2025)

• NarraDetect: An annotated dataset for the task of narrative detection (Piper & Bagga, WNU 2025)

ACL

• Andrew Piper and Sunyam Bagga. 2025. NarraDetect: An annotated dataset for the task of narrative detection. In Proceedings of the The 7th Workshop on Narrative Understanding, pages 1–7, Albuquerque, New Mexico. Association for Computational Linguistics.