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

TL;DR

This paper introduces a new dataset and a two-stage method for personalizing story generation by inferring and mimicking individual authors' writing styles, improving alignment with their past work.

Contribution

It presents Mythos, a large dataset of stories from multiple authors, and a novel pipeline for inferring author styles and generating personalized stories.

Findings

Personalized stories outperform non-personalized baselines in style capture.

Stories from Reddit are easier to personalize.

Creativity and language use are easier to personalize than plot.

Abstract

Personalization is critical for improving user experience in interactive writing and educational applications, yet remains understudied in story generation. We study the task of personalizing story generation, where our goal is to mimic an author's writing style, given other stories written by them. We collect Mythos, a dataset of 3.6k stories from 112 authors, with an average of 16 stories per author, across five distinct sources reflecting diverse story-writing settings. We propose a two-stage pipeline for personalized story generation: first, we infer authors' implicit writing characteristics and organize them into an Author Writing Sheet, which is validated by humans to be of high quality; second, we simulate the author's persona using tailored persona descriptions and personalized story rules. We find that stories personalized using the Author Writing Sheet outperform a non-personalized baseline, achieving a 78% win-rate in capturing authors' past style and 59% in similarity to ground-truth author stories. Human evaluation supports these findings and further highlights trends, such as Reddit stories being easier to personalize, and the Creativity and Language Use aspects of stories being easier to personalize than the Plot.