2024/11/19 by Lion Schulz, Schulz, Lion, Miguel Patrício +3
Computer Science · #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2411.12907
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.