| Challenge: | Story salads are mixtures of multiple documents that can be generated at scale . they exhibit challenging inference problems, and require global context and coherence . |
| Approach: | They propose to generate salads that exhibit challenging inference problems by exploiting the Wikipedia hierarchy . they propose a task where the objective is to group sentences from the same narratives . |
| Outcome: | The proposed task is based on a novel, challenging clustering task using Wikipedia . it is difficult to identify relevant information and assemble it into coherent narratives . |
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| Challenge: | a hierarchical model that generates a premise and then conditions on it creates fluent text . a novel form of model fusion improves the relevance of the story to the prompt . |
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Tell Me Again! a Large-Scale Dataset of Multiple Summaries for the Same Story (2024.lrec-main)
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| Challenge: | Existing approaches to represent narratives on short-form texts are limited as narrative semantics are an open class. |
| Approach: | They propose to use Wikipedia summaries as a proxy for entire stories or for analysis of the summary itself. |
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Improving Neural Story Generation by Targeted Common Sense Grounding (D19-1)
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| Challenge: | Recent advances in language modeling have yielded thematic and stylistic coherence in story generation through large scale pretraining of Transformer models. |
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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. |
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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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Guiding Neural Story Generation with Reader Models (2022.findings-emnlp)
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| Challenge: | Existing systems that generate narratives with neural language models require substantial knowledge engineering of logical constraints, limiting their generality. |
| Approach: | They propose a framework in which a reader model is used to reason about the storyshould progress. |
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Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes (N18-2)
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| Challenge: | Existing methods to identify instances of similar narratives are limited by annotated data. |
| Approach: | They propose a task for identifying instances of similar narratives from a collection of narrative texts. |
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Revisiting Sentence Union Generation as a Testbed for Text Consolidation (2023.findings-acl)
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| Challenge: | In order to acquire knowledge on a new subject, it is often necessary to consult multiple sources of written information. |
| Approach: | They propose to revisit the sentence union generation task as an effective well-defined testbed for assessing text consolidation capabilities. |
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Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations (2022.naacl-main)
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| Challenge: | Existing datasets for sentence fusion tasks are limited in size and scope . despite recent advances, cross-document tasks such as multi-document summarization have not progressed with the same pace. |
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Query-focused Scenario Construction (D19-1)
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| Challenge: | Stronger neural network models and harder synthetic training settings are important to achieve high performance. |
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