Challenge: Existing methods for long-form story generation rely on rigid outlines or lack macro-level planning, making it difficult to achieve contextual consistency and coherent plot development.
Approach: They propose a Dynamic Hierarchical Outlining with Memory-Enhancement long-form story generation method to generate long-formed story with coherent content and plot.
Outcome: The proposed method significantly improves the fluency, coherence, and overall quality of generated long stories compared to state-of-the-art methods.

Similar Papers

DOC: Improving Long Story Coherence With Detailed Outline Control (2023.acl-long)

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Challenge: Detailed Outline Control (DOC) framework improves long-range plot coherence . human evaluations of DOC show it outperforms strong Re3 on plot cohesion, outline relevance and interestingness .
Approach: They propose a Detailed Outline Control framework to improve long-range plot coherence . the detailed outliner creates a more detailed, hierarchically structured outline . they propose doc with a detailed controller to ensure the more detailed outline is respected .
Outcome: The proposed framework outperforms Re3 on plot coherence, outline relevance and interestingness.
Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries (2022.coling-1)

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Challenge: Existing models focus on local word prediction, and cannot make high level plans on what to generate.
Approach: They propose a pipelined system that summarises, outlines and elaborates on each bullet point to generate the corresponding segment.
Outcome: The proposed system produces long texts with significantly better quality and faster convergence speed.
Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation (2025.findings-emnlp)

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Challenge: Existing methods for generating readable outlines are inability to segment long texts .
Approach: They propose an unsupervised framework to guide large language model outline generation . framework ensures each structured plot encapsulates complete causality by accurately identifying plot boundaries.
Outcome: The proposed framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries.
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 .
Approach: They use a hierarchical model that first generates a premise, then transforms it into a text . they use fusion to improve relevance of the story to the prompt and add a gated mechanism to model context .
Outcome: The proposed model improves on strong baselines on automated and human evaluations.
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence (2021.acl-long)

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Challenge: Existing generation models struggle to maintain a coherent event sequence throughout the generated text.
Approach: They propose a long text generation model which can represent prefix sentences at sentence level and discourse level in the decoding process.
Outcome: The proposed model can generate more coherent texts than state-of-the-art models.
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.
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.
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 .
NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization (2025.acl-long)

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Challenge: Summarizing long-form narratives requires capturing intricate plotlines, character interactions, and thematic coherence over tens of thousands of tokens.
Approach: They propose a multi-agent LLM framework for narrative summarization that processes long-form text through a structured pipeline without fine-tuning.
Outcome: The proposed framework achieves 30.0% improvement in BERTScore across books, movies, and TV scripts.
StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation (2026.acl-long)

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Challenge: Existing approaches augment reasoning steps or inject specific structure into how models think, but leave scattered problem conditions unchanged.
Approach: They propose a narrative reformulation framework that transforms code generation questions into coherent natural language narratives.
Outcome: The proposed framework improves the performance of 11 code generation models on HumanEval, LiveCodeBench, and CodeForces.
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation (2025.findings-acl)

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Challenge: Existing methods for storytelling lack coherence and consistency, compromising the overall storytelling experience.
Approach: They propose a novel approach that improves the coherence and consistency of automatically generated stories by managing plot nodes and enabling dynamic interactions between different parts of the story.
Outcome: The proposed approach outperforms existing methods in 84.33% of the trials.

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