M o PS : Modular Story Premise Synthesis for Open-Ended Automatic Story Generation (2024)

Abstract: 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) Pre-collect 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 7.5k generated premises and 1k extended stories.

## experiment results and analysis our experiments focus on three scenarios: (1) evaluate premise diversity and quality: whether mops produces more diverse and higher-quality premises than baselines. (2) component ablation: the effectiveness of mops's modular design and sequential module dependencies. (3) long story quality assessment: whether mops premises yield higher quality long stories. significance tests. we conduct significance tests to verify results' validity. our significance tests evaluate: (a) if mops's premises and long stories outperform baselines; (b) if masking mops components impacts premise quality. ## conclusion this paper presents mops, a modular approach that automates the design and creation of story premises. using mops, we synthesized a large number of diverse and high-quality premises, generating extended novels and scripts. thorough evaluation demonstrates the superiority of mops over multiple baselines. similarly, extended stories from our premises also exhibit higher quality. based on our premises and extended stories, we created three versions of premise-story dataset to accommodate research for varied research scales. future asg frameworks can benefit from these premises for thorough effectiveness evaluation. we believe our research will advance the field of automated story generation. looking to the future, we hope to explore the impact of premises on cross-modal story creation, such as story poster generation @xcite , graphic narratives @xcite , and even video stories @xcite .