| Challenge: | Recent work has focused on identifying narrative elements in personal stories texts, but this paper focuses on informational texts. |
| Approach: | They propose a novel NLP task for detecting narrative elements in raw text by adapting elements from the oral narrative theory of Labov and Waletzky and adding a new narrative element of their own. |
| Outcome: | The proposed scheme achieves an average F1 score of 0.77 and is better suited for informational texts than the oral narrative theory. |
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| Challenge: | Story components, namely events, time, participants, and their relations, are present in narrative texts from different domains such as journalism, medicine, finance, and law. |
| Approach: | They propose to use an array of narrative extraction tools to extract narratives from text . the package contains an array and an experimental module for evaluation . |
| Outcome: | The text2story python supports the narrative extraction and visualization pipeline. |
Exploring Text Recombination for Automatic Narrative Level Detection (2022.lrec-1)
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| Challenge: | Existing annotation workflows do not scale well to the annotation of complex narrative phenomena. |
| Approach: | They propose a workflow for narrative level detection that includes operationalization and a model . they propose generating training data synthetically to improve the prediction results . |
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PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles (2025.acl-long)
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Nikolaos Nikolaidis, Nicolas Stefanovitch, Purificação Silvano, Dimitar Iliyanov Dimitrov, Roman Yangarber, Nuno Guimarães, Elisa Sartori, Ion Androutsopoulos, Preslav Nakov, Giovanni Da San Martino, Jakub Piskorski
| Challenge: | a new dataset of news articles annotated for narratives provides a framework for narrative detection . recurring narratives can propagate with very high velocity across audiences, languages and countries . |
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Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) excel in generating coherent texts, but their ability to comprehend the author’s thoughts remains uncertain. |
| Approach: | They conduct a comprehensive survey of narrative understanding tasks, examining their key features, definitions, taxonomy, associated datasets, evaluation metrics, and limitations. |
| Outcome: | The proposed framework could be extended to address novel narrative understanding tasks. |
Narrative Theory for Computational Narrative Understanding (2021.emnlp-main)
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| Challenge: | a growing body of theoretical work on narrative has been focused on the field of natural language processing . this position paper aims to provide a unifying framework for the computational study of narrative . |
| Approach: | They propose to introduce dominant theoretical frameworks to the NLP community and situate current research within distinct narratological traditions. |
| Outcome: | The proposed framework would allow for new empirical questions and applications in the field of natural language processing. |
Narrative Embedding: Re-Contextualization Through Attention (2021.emnlp-main)
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| Challenge: | a novel approach to narrative event representation uses attention to re-contextualize events across the whole story . a recent study shows that attention is used to attach event semantics to tokens . |
| Approach: | They propose an unsupervised approach to narrative event representation using attention to re-contextualize events across the whole story. |
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Neural Storyline Extraction Model for Storyline Generation from News Articles (N18-1)
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| Challenge: | Existing approaches to storyline generation are domain dependent and cannot deal with unseen event types. |
| Approach: | They propose a neural network-based approach to extract structured representations and evolution patterns of storylines without using annotated data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on accuracy and efficiency on three news corpora and it is based on supervised models. |
Identifying Informational Sources in News Articles (2023.emnlp-main)
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| Challenge: | Identifying sources of information in news articles is relevant to many tasks in NLP, including misinformation detection and argumentation. |
| Approach: | They propose a task to study compositionality of sources in news articles to understand how they are chosen to complement each other. |
| Outcome: | The proposed dataset can be used to train high-performing models for information detection and source attribution. |
Automatic Focus Annotation: Bringing Formal Pragmatics Alive in Analyzing the Information Structure of Authentic Data (N18-1)
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| Challenge: | Using focus-background dichotomy, discourse and information structure of sentences are being studied in context. |
| Approach: | They propose to automate the analysis of focus in authentic written data by using a range of lexical, syntactic, and semantic features to achieve an accuracy of 78.1%. |
| Outcome: | The proposed approach achieves 78.1% accuracy for identifying focus in authentic written data. |
CANarEx: Contextually Aware Narrative Extraction for Semantically Rich Text-as-data Applications (2022.findings-emnlp)
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| Challenge: | Narrative modelling is a field of active research that conceptualizes narratives as connected entity chains. |
| Approach: | They propose an alternative narrative extraction approach using semantic role labeling to extract tuples from text, then dimensionality reduction to reduce the space of entities and connections separately. |
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