Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement (2412.13575v1)

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.

• The paper introduces DOME, a framework that combines dynamic hierarchical outlining and memory-enhancement to enhance narrative coherence.
• It employs a plan-write framework with adaptive outlining that refines story structure as the narrative evolves.
• Experimental results show a 6.87% increase in plot diversity and an 87.61% reduction in contextual conflicts, underscoring its effectiveness.

Overview of "Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement"

The paper "Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement" presents a novel framework named DOME for generating coherent long-form stories. The proposed approach addresses key limitations in existing methods for long-form story generation, particularly those intrinsic to LLMs. These limitations include memory constraints due to LLMs' black-box self-attention mechanisms that struggle with long-range dependencies, and inadequate macro-level planning that hampers coherent plot development. DOME innovatively integrates a Dynamic Hierarchical Outline (DHO) mechanism alongside a Memory-Enhancement Module (MEM), leveraging temporal knowledge graphs for enhanced consistency and coherence in story generation.

Methodology

The DOME framework constitutes two core components: a Dynamic Hierarchical Outlining (DHO) mechanism and a Memory-Enhancement Module (MEM).

1. Dynamic Hierarchical Outlining (DHO):

• Rough and Detailed Outline Planning: The DHO mechanism applies a plan-write framework informed by novel writing theory. It initially generates a rough outline ensuring plot completeness by adhering to established storytelling structures. Following this, detailed outlines are dynamically generated, adapting to evolving story contexts to enhance plot fluency.
• Adaptive Planning: Unlike static frameworks, the detailed outline in DHO is refined based on preceding story content, thus adjusting to uncertainties during the writing process. This dynamic refinement is intended to improve overall plot coherence and narrative flow.

1. Memory-Enhancement Module (MEM):

• Contextual Information Management: MEM utilizes temporal knowledge graphs to document and access prior story content, effectively reducing contextual conflicts. These graphs facilitate quick retrieval of relevant information, acting as a memory aid that maintains coherence across extended narratives.
• Conflict Detection: A Temporal Conflict Analyzer within MEM automatically assesses consistency by identifying and addressing temporal conflicts, thereby improving alignment with human preferences for narrative flow.

Experimental Evaluation

The efficacy of DOME was assessed through experimental comparisons with state-of-the-art methods. Key findings indicate that DOME outperforms existing techniques regarding plot coherence and contextual consistency:

• Coherence Metrics: DOME demonstrated improved plot fluency, with a notable 6.87% increase in diversity as measured by the ${Ent}$-2 metric. This result underscores its effectiveness in producing diverse and non-repetitive narratives.
• Conflict Reduction: The MEM component of DOME significantly decreased contextual conflicts, reducing them by 87.61%, thereby affirming the framework’s ability to maintain narrative consistency.

Implications and Future Directions

The research presented in this paper holds substantial implications for automated narrative generation, paving the way for more sophisticated story generation systems capable of producing coherent long-form texts. Practically, this could enhance applications in novel writing and interactive storytelling, where narrative coherence is crucial. The integration of hierarchical outlining with memory enhancement could also inspire future research in narrative generation, particularly for tasks necessitating long-term dependency handling and adaptive content creation.

Looking forward, extending this approach to incorporate even more sophisticated memory architectures and further refining the dynamic planning mechanisms could yield additional improvements. Additionally, exploring how these methods can be tailored to specific genres or styles could open up new opportunities for creating AI-driven content generation platforms tailored to distinct user needs.

In conclusion, the introduction of DOME offers a promising direction for tackling challenges associated with long-form story generation, advancing both the theoretical understanding and practical applications of narrative coherence technology.