MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation
This addresses the need for scalable, high-quality story premises in open-ended automatic story generation, representing an incremental improvement over existing approaches.
The paper tackled the problem of limited diversity and scalability in story premises for automatic story generation by introducing Modular Story Premise Synthesis (MoPS), which breaks down premises into modules for automated design and generation, resulting in synthesized premises that excel in diversity, fascination, completeness, and originality compared to existing methods.
A story premise succinctly defines a story's main idea, foundation, and trajectory. It serves as the initial trigger in automatic story generation. Existing sources of story premises are limited by a lack of diversity, uneven quality, and high costs that make them difficult to scale. In response, we introduce Modular Story Premise Synthesis (MoPS) which breaks down story premises into modules like background and persona for automated design and generation. MoPS consists of three phases: (1) Precollect a consistent set of candidates for each module to form a nested dictionary. (2) Extract a key path from the nested dictionary as the premise design. (3) Instruct an LLM to integrate the design into a coherent premise sentence. Thorough evaluations demonstrate that our synthesized premises excel in diversity, fascination, completeness, and originality compared to those induced from large language models and captured from public story datasets. Similarly, the extended novels and scripts generated from our premises also exhibit higher quality. In supplementary materials, we provide the MoPS code suite, along with 7.6k generated premises and 1k extended stories. Code: https://github.com/GAIR-NLP/MoPS.