Challenge: Narratives and argumentation are deeply related, according to psychologists and social scientists.
Approach: They annotated StoryARG from well-established corpora in computational argumentation and the Social Sciences, as well as comments to New York Times articles.
Outcome: The dataset contains 2451 textual spans annotated at two levels . it reveals positive impact on effectiveness for stories which illustrate a solution to a problem and in general, annotator-specific preferences .

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Reports of personal experiences and stories in argumentation: datasets and analysis (2022.acl-long)

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Challenge: Personal experiences and stories are important in argumentation, but they are not considered in the social sciences.
Approach: They propose to use annotated documents to scale-up the analysis using existing annotations.
Outcome: The proposed classifiers can identify documents containing personal experiences and reports . they can scale up to three domains and show that they perform well across domains.
A Multi-layer Annotated Corpus of Argumentative Text: From Argument Schemes to Discourse Relations (L18-1)

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Challenge: Recent interest in Argumentation Mining has brought to the fore the need for corpora annotated with argument information, which can be used as training data.
Approach: They propose a set of guidelines for the annotation of argument schemes and a new annotation tool for the 'inferential' argument schemes.
Outcome: The proposed corpus includes 112 argumentative microtexts and a new annotation tool.
Social Story Frames: Contextual Reasoning about Narrative Intent and Reception (2026.acl-long)

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Challenge: SocialStoryFrames is a formalism for distilling plausible inferences about reader response . authors characterize frequency and interdependence of storytelling intents across communities .
Approach: They propose a formalism for distilling plausible inferences about reader response using conversational context and a taxonomy grounded in narrative theory, linguistic pragmatics, and psychology.
Outcome: The proposed model can be used to analyze reader responses in online communities.
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)

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Challenge: Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies .
Approach: They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT .
Outcome: The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory.
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating (P19-1)

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Challenge: Existing argumentation datasets have allowed only limited assessment of "user" traits because information on background of users is generally unavailable.
Approach: They present a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles.
Outcome: The proposed dataset includes 78,376 debates generated over a 10-year period along with comprehensive participant profiles.
A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)

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Challenge: Existing approaches to modeling media narratives miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability.
Approach: They propose a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering.
Outcome: The proposed framework produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation.
CLAUSE-ATLAS: A Corpus of Narrative Information to Scale up Computational Literary Analysis (2024.lrec-main)

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Challenge: XIX and XX century English novels annotated automatically contain 41,715 labeled clauses . a new approach to analyze novels based on clauses captures structural patterns within books, as well as qualitative differences between them.
Approach: They propose to use a corpus of XIX and XX century English novels annotated automatically to study stories as sequences of eventive, subjective and contextual information.
Outcome: The proposed method captures structural patterns within books, as well as qualitative differences between them.
Stories and Personal Experiences in the COVID-19 Discourse (2024.lrec-main)

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Challenge: 'storytelling' is a human strategy to use personal experiences to back-up one's position in debates about controversial topics.
Approach: They analyse the use of storytelling in the COVID-19 discourse by automatically annotating three publicly available Reddit datasets for a total of 367K comments.
Outcome: The proposed analysis on three publicly available Reddit datasets shows that storytelling is a powerful argumentative tool.
Are Large Language Models Capable of Generating Human-Level Narratives? (2024.emnlp-main)

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Challenge: a recent HCI study has pointed to gaps in machine storytelling ability at the global level . authors show that LLMs have less suspense and less tension than human stories .
Approach: They propose a computational framework to analyze narratives through three discourse-level aspects.
Outcome: The proposed framework analyzes narratives through three discourse-level aspects . it shows that LLMs fall short of human abilities in discourse understanding .
Modeling Persuasive Discourse to Adaptively Support Students’ Argumentative Writing (2022.acl-long)

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Challenge: Argumentation is an omnipresent rudiment of daily communication and thinking . humans struggle to develop argumentation skills due to a lack of individual and instant feedback in their learning process.
Approach: They propose an argumentation annotation approach to model argumentative discourse in student-written business model pitches and embed it into an adaptive writing support system for students that provides individual argumentation feedback.
Outcome: The proposed method annotates a corpus of 200 business model pitches in german and measures their self-efficacy and ease-of-use in a real-world writing exercise.

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