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. |
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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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Neural Text Generation in Stories Using Entity Representations as Context (N18-1)
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| Challenge: | Existing models of text generation that explicitly represent entities are based on the use of words and entities. |
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A Case Study on Neural Headline Generation for Editing Support (N19-2)
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Kazuma Murao, Ken Kobayashi, Hayato Kobayashi, Taichi Yatsuka, Takeshi Masuyama, Tatsuru Higurashi, Yoshimune Tabuchi
| Challenge: | a news-aggregator is a website or mobile application that aggregates web content . dozens of professional editors manually create their headlines, which are much shorter than the original headlines. |
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NGEP: A Graph-based Event Planning Framework for Story Generation (2022.aacl-short)
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| Challenge: | Current approaches to story generation are based on end-to-end neural generation models, such as BART, to generate event sequences. |
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Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
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| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
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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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Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)
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| Challenge: | Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts . |
| Approach: | They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events. |
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End-to-End Segmentation-based News Summarization (2022.findings-acl)
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| Challenge: | Existing summarization systems only provide one genetic summary of the whole article, making it difficult for users to navigate the reading. |
| Approach: | They propose a task of segmenting a news article into multiple sections and generating the corresponding summary to each section. |
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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. |
| Approach: | They propose a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. |
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Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics (2022.aacl-short)
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| Challenge: | Current neural models for Chinese story generation struggle to generate high-quality long text narratives due to ambiguity in syntactically parsing the Chinese language. |
| Approach: | They propose a framework that enhances the feature capturing mechanism by informing the generation model of dependencies between words and additionally augmenting the semantic representation learning through synonym denoising training. |
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