| Challenge: | Existing language models generate word by word, but fail to capture high-level interactions . a novel decomposition approach allows more abstract representations to be generated first . |
| Approach: | They propose models which abstract over actions and entities to create stories . they generate predicate-argument structure, then replace placeholders with context-sensitive names . |
| Outcome: | The proposed models improve diversity and coherence of events and entities in generated stories. |
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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. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |
Content Planning for Neural Story Generation with Aristotelian Rescoring (2020.emnlp-main)
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| Challenge: | Current approaches to narrative composition are plagued by difficulty in mastering structure, will veer between topics, and lack long-range cohesion. |
| Approach: | They propose a plot-generation language model and a set of rescoring models that implement an aspect of good story-writing as detailed in Aristotle's Poetics. |
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Hierarchical Neural Story Generation (P18-1)
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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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A Survey on LLMs for Story Generation (2025.findings-emnlp)
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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
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Stylized Story Generation with Style-Guided Planning (2021.findings-acl)
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| Challenge: | Current storytelling systems focus more on generating stories with coherent plots regardless of the narration style. |
| Approach: | They propose a novel task, stylized story generation, that first plans stylized keywords and then generates the whole story with the guidance of the keywords. |
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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. |
| Approach: | They propose a neural model that explicitly represents entities mentioned in the text . they use vectors that are updated as the text proceeds to improve automatic evaluations . |
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Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning (2022.findings-emnlp)
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| Challenge: | Existing methods to automate story generation focus on single-character stories and lack basiccommonsense reasoning. |
| Approach: | They propose a commonsense-inference Augmentedneural StoryTelling framework that introduces commonsensical reasoning into the story generation process. |
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Little Red Riding Hood Goes around the Globe: Crosslingual Story Planning and Generation with Large Language Models (2024.lrec-main)
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| Challenge: | Existing work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English. |
| Approach: | They propose a task of crosslingual story generation with planning to leverage the creative and reasoning capabilities of large pretrained language models to generate stories in multiple languages. |
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Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
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| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
Story Generation with Rich Details (2020.coling-main)
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| Challenge: | Recent neural story generation systems have been able to produce coherent stories. |
| Approach: | They propose a model that features an outliner, which proceeds the main story line to realize global coherence, and a detailer, which supplies relevant details to the story in a locally coherent manner. |
| Outcome: | The proposed model outperforms baseline models in the informativeness and coherence tests on human participants. |