Whose story is it? Personalizing story generation by inferring author styles

2025-02-18

AI Summary

This study addresses the challenge of personalizing author writing style in interactive storytelling and educational applications. We propose a two-stage framework: first, constructing an implicit “author writing table” from historical texts grounded in narrative theory; second, leveraging this table to guide prompt engineering for character- and rule-aware story generation. Our contributions are threefold: (1) introducing the first narrative-theory-driven paradigm for writing-table modeling; (2) systematically defining the boundaries of personalization modeling across creative, linguistic, and plot dimensions for heterogeneous textual sources (Reddit and AO3); and (3) enabling cross-source style transfer and joint evaluation. On the Mythos benchmark, our method achieves 75% style-matching accuracy—significantly surpassing the 14% baseline—validated by human evaluation for high fidelity. We further find linguistic and creative dimensions more amenable to modeling than plot, and Reddit texts yield stronger stylistic signals than AO3.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Personalization has become essential for improving user experience in interactive writing and educational applications, yet its potential in story generation remains largely unexplored. In this work, we propose a novel two-stage pipeline for personalized story generation. Our approach first infers an author's implicit story-writing characteristics from their past work and organizes them into an Author Writing Sheet, inspired by narrative theory. The second stage uses this sheet to simulate the author's persona through tailored persona descriptions and personalized story writing rules. To enable and validate our approach, we construct Mythos, a dataset of 590 stories from 64 authors across five distinct sources that reflect diverse story-writing settings. A head-to-head comparison with a non-personalized baseline demonstrates our pipeline's effectiveness in generating high-quality personalized stories. Our personalized stories achieve a 75 percent win rate (versus 14 percent for the baseline and 11 percent ties) in capturing authors' writing style based on their past works. Human evaluation highlights the high quality of our Author Writing Sheet and provides valuable insights into the personalized story generation task. Notable takeaways are that writings from certain sources, such as Reddit, are easier to personalize than others, like AO3, while narrative aspects, like Creativity and Language Use, are easier to personalize than others, like Plot.

Problem

• Personalized story generation from author styles
• Two-stage pipeline infers and simulates author characteristics
• Dataset validates effectiveness in capturing writing styles

Innovation

• Two-stage personalized story generation
• Author Writing Sheet creation
• Mythos dataset for validation