Narrative Information Theory
Multimedia
2024-11-21 v1 Information Theory
math.IT
Abstract
We propose an information-theoretic framework to measure narratives, providing a formalism to understand pivotal moments, cliffhangers, and plot twists. This approach offers creatives and AI researchers tools to analyse and benchmark human- and AI-created stories. We illustrate our method in TV shows, showing its ability to quantify narrative complexity and emotional dynamics across genres. We discuss applications in media and in human-in-the-loop generative AI storytelling.
Cite
@article{arxiv.2411.12907,
title = {Narrative Information Theory},
author = {Lion Schulz and Miguel Patrício and Daan Odijk},
journal= {arXiv preprint arXiv:2411.12907},
year = {2024}
}
Comments
To be published in NeurIPS 2024 Workshop on Creativity & Generative AI. 7 pages, 3 figures