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        <title>MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation
%A Ma, Yan
%A Qiao, Yu
%A Liu, Pengfei
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F ma-etal-2024-mops
%X 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.
%U https://aclanthology.org/2024.acl-long.117
%P 2135-2169

Markdown (Informal)

[MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation](https://aclanthology.org/2024.acl-long.117) (Ma et al., ACL 2024)

• MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation (Ma et al., ACL 2024)

ACL

• Yan Ma, Yu Qiao, and Pengfei Liu. 2024. MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2135–2169, Bangkok, Thailand. Association for Computational Linguistics.