Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement (2025)

Abstract: 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.

## introduction among natural language processing (nlp) tasks, the automatic generation of a long-form story is a representative task that requires creativity and long-term planning skills. by learning from humanwritten stories, an automated storyteller mimics humans and becomes competent in producing stories useful for various application scenarios, such as novel writing and interactive storytelling @xcite . large language models (llms) are rapidly developing, generating a long-form story that further increases dramatically in length, complexity, and fluency @xcite . unfortunately, it is difficult for the llms to generate a long-form story maintaining contextual consistency in semantic and coherent plot development for the following reasons. 1) memory limitations: the black-box self-attention mechanism forms the core of llms for contextual connections and still suffers from the long-range dependency issue @xcite , making it hard for them to precisely and unambiguously recall and thereby leading to contextual incoherence. 2) planning difficulties: llms cannot effectively apply knowl-edge of coherent plot planning partly because their training involves learning from vast datasets that include a variety of texts, but they do not inherently understand or apply the principles of storytelling and thus hinders the generation of engaging stories with complete and fluent plots @xcite . existing long-form story generation methods often leverage higher-level attributes such as plots or commonsense knowledge @xcite , primarily aiming to enhance story development fluency, and can be divided into two kinds based on whether humans are involved. the first category emulates the human writing process by adopting a plan-and-write framework @xcite in generating the long-form story (see fig. 1(a) ). this approach separates the generation process into the planning and writing stages, utilizing detailed and plot-fluent outlines to guide the writing phase. while these methods can extend the length of the story and improve plot development fluency, the inflexibility of a fixed outline can impede its adaptability to uncertainty in the writing stage, often leading to plot incoherence, such as contextual repetition or conflicts. other works @xcite explore generating content progressively without outlining based on human interaction and relevant preceding content (see fig. 1 (b) ). they can address the influence of local plot development caused by uncertainty in generation. however, the generated story lacks a macro-level of rational planning, affecting the plot completeness. to address the above limitations, we propose dynamic hierarchical outlining with memory-enhancement long-form story generation method, called dome, to generate the long-form story with coherent content and plot (see fig. 1(c) ). dome is based on the collaboration of a dynamic hierarchical outline (dho) mechanism and a memory-enhancement module (mem) to generate long-form story. specifically, dho mechanism to guide long-form story generation based on the plan-write writing framework @xcite and the novel-writing theory @xcite . the dho mechanism requires the generation of a rough outline conforming to the novel writing theory to ensure plot completeness, and the dynamic planning of a detailed outline based on the rough outline during the writing process so that it can adapt to the uncertainty of the generation and improve the plot fluency. the mem stores and accesses generated stories through temporal knowledge graphs and provides contextual content for outline planning and story writing to reduce content conflicts in long story texts. to automatically evaluate the contextual consistency, we propose a conflict detection matrix, temporal conflict analyzer which is based on the information representation rules of the temporal knowledge graph and llm. our main contributions are as follows: ## proposed method we propose a dynamic hierarchical outlining with memory-enhancement long-form story generation method, named dome, aiming to improve the story coherence from plot and context description. based on the advanced understanding and generation ability of llm @xcite , dome is mainly composed of two parts: the dynamic hierarchical outline module (dho) and the kg-based memory-enhancement module (mem). as shown in fig. 2 , given the user input i, including story setting, character introduction, and storyline requirements, the rough outlines of a story are planned at a time under the guidance of the writing theory, ensuring the completeness of the plot. then, the detailed outline and the corresponding part of the story are generated alternately. it means the detailed outlines are not expanded by the rough outline incrementally until its previous stories are generated, enabling it to adjust to the uncertainty of the previously generated stories, and then the detailed outlines guide the generation of sequential stories. mem stores user input at the very beginning and the generated story during the writing process. the module provides relevant content in natural language for the generation stage, ensuring contextual consistency. through the collaboration of these two modules, dome improve the story's coherence from the plot and contextual consistency. ## conclusion in this paper, we propose a dynamic hierarchical outlining with memory enhancement, named dome, to generate a coherent long-form story. specifically, we propose a dynamic hierarchical outline (dho) mechanism for long-form story generation, based on the plan-write framework and novel-writing theory. the dho mechanism creates a rough outline that aligns with novel-writing theory and dynamically generates it during the writing process, enhancing plot fluency. additionally, the memory-enhancement module (mem) uses temporal knowledge graphs to store and access generated stories, providing contextual content for both detailed outline planning and story writing, and reducing contextual conflicts. lastly, the temporal conflict analyzer detects potential conflicts using temporal knowledge graphs and integrates with llms to automatically assess the contextual consistency of the generated text. experiments demonstrate that dome significantly improves the coherence and overall quality of generated longform stories from plot and expression compared to sota methods.