# Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement

arXiv: https://arxiv.org/abs/2412.13575 · HTML: https://mlsanity.com/paper/2412.13575/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [Prompting GPT-3 To Be Reliable](https://mlsanity.com/paper/2210.09150/) (2022)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022)
- [GreaseLM: Graph REASoning Enhanced Language Models for Question Answering](https://mlsanity.com/paper/2201.08860/) (2022)
- [LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs](https://mlsanity.com/paper/2408.07055/) (2024)
- [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https://mlsanity.com/paper/1809.05972/) (2018)
- [Narrative Planning: Balancing Plot and Character](https://mlsanity.com/paper/1401.3841/) (2014)
- [The Next Chapter: A Study of Large Language Models in Storytelling](https://mlsanity.com/paper/2301.09790/) (2023)
- [DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation](https://mlsanity.com/paper/2109.08877/) (2021)
- [JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering](https://mlsanity.com/paper/2112.02732/) (2021)
- [Integrating Graph Contextualized Knowledge into Pre-trained Language Models](https://mlsanity.com/paper/1912.00147/) (2019)
- [Improving Pacing in Long-Form Story Planning](https://mlsanity.com/paper/2311.04459/) (2023)
- [Cue Me In: Content-Inducing Approaches to Interactive Story Generation](https://mlsanity.com/paper/2010.09935/) (2020)

## Built on by (most cited first, 30 of 30)

- [Test-Time Learning for Large Language Models](https://mlsanity.com/paper/2505.20633/) (2025)
- [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://mlsanity.com/paper/2509.13312/) (2025)
- [StoryWriter: A Multi-Agent Framework for Long Story Generation](https://mlsanity.com/paper/2506.16445/) (2025)
- [A Cognitive Writing Perspective for Constrained Long-Form Text Generation](https://mlsanity.com/paper/2502.12568/) (2025)
- [ViMax: Agentic Video Generation](https://mlsanity.com/paper/2606.07649/) (2026)
- [Can LLMs Automate Fact-Checking Article Writing?](https://mlsanity.com/paper/2503.17684/) (2025)
- [Lost in Stories: Consistency Bugs in Long Story Generation by LLMs](https://mlsanity.com/paper/2603.05890/) (2026)
- [SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models](https://mlsanity.com/paper/2506.04180/) (2025)
- [The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking](https://mlsanity.com/paper/2602.09516/) (2026)
- [ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research](https://mlsanity.com/paper/2606.20122/) (2026)
- [Long Story Generation via Knowledge Graph and Literary Theory](https://mlsanity.com/paper/2508.03137/) (2025)
- [StoryAlign: Evaluating and Training Reward Models for Story Generation](https://mlsanity.com/paper/2605.04831/) (2026)
- [NARRA-Gym for Evaluating Interactive Narrative Agents](https://mlsanity.com/paper/2605.08503/) (2026)
- [StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment](https://mlsanity.com/paper/2605.28073/) (2026)
- [When Stories Evolve: Benchmarking LLM Storytelling Across Agent Architectures in Open-Ended World Simulations](https://mlsanity.com/paper/2608.15654/) (2026)
- [Measuring Information Distortion in Hierarchical Ultra long Novel Reconstruction:The Optimal Expansion Ratio](https://mlsanity.com/paper/2505.12572/) (2025)
- [Codified Foreshadowing-Payoff Text Generation](https://mlsanity.com/paper/2601.07033/) (2026)
- [Beyond Model Scaling: Test-Time Intervention for Efficient Deep Reasoning](https://mlsanity.com/paper/2601.11252/) (2026)
- [BiT-MCTS: A Theme-based Bidirectional MCTS Approach to Chinese Fiction Generation](https://mlsanity.com/paper/2603.14410/) (2026)
- [Story2Proposal: A Scaffold for Structured Scientific Paper Writing](https://mlsanity.com/paper/2603.27065/) (2026)
- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)
- [EvoSpark: Endogenous Interactive Agent Societies for Unified Long-Horizon Narrative Evolution](https://mlsanity.com/paper/2604.12776/) (2026)
- [Towards Human-Level Book-Writing Capability](https://mlsanity.com/paper/2605.17064/) (2026)
- [Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation](https://mlsanity.com/paper/2606.12599/) (2026)
- [Steering Emotional Dynamics for Art Therapy: Controllable Narrative Script Generation through Hierarchically Guided LLM Agents](https://mlsanity.com/paper/2606.16481/) (2026)
- [When to Plan, When to Polish: Noise Level as a Granularity Axis for Diffusion Language Models](https://mlsanity.com/paper/2606.21802/) (2026)
- [ConWriter: Transition-Constrained Stateful Long-Form Story Generation with Lightweight Neuro-Symbolic Consistency Control](https://mlsanity.com/paper/2608.05169/) (2026)
- [Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction](https://mlsanity.com/paper/2609.06731/) (2026)
- [Shared Worlds, Private Minds: Structured Memory for Long-Form Writing as World Creation](https://mlsanity.com/paper/2609.32401/) (2026)
- [Scaling Long-Form Story Generation via Narrative State Tracking](https://mlsanity.com/paper/2609.35759/) (2026)

## Abstract (arXiv)

Long-form story generation task aims to produce coherent and sufficiently lengthy text, essential for applications such as novel writingand interactive storytelling. However, existing methods, including LLMs, rely on rigid outlines or lack macro-level planning, making it difficult to achieve both contextual consistency and coherent plot development in long-form story generation. To address this issues, we propose Dynamic Hierarchical Outlining with Memory-Enhancement long-form story generation method, named DOME, to generate the long-form story with coherent content and plot. Specifically, the Dynamic Hierarchical Outline(DHO) mechanism incorporates the novel writing theory into outline planning and fuses the plan and writing stages together, improving the coherence of the plot by ensuring the plot completeness and adapting to the uncertainty during story generation. A Memory-Enhancement Module (MEM) based on temporal knowledge graphs is introduced to store and access the generated content, reducing contextual conflicts and improving story coherence. Finally, we propose a Temporal Conflict Analyzer leveraging temporal knowledge graphs to automatically evaluate the contextual consistency of long-form story. Experiments demonstrate that DOME significantly improves the fluency, coherence, and overall quality of generated long stories compared to state-of-the-art methods.