  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.
