Narrative Information Theory
| Autor: | Schulz, Lion, Patrício, Miguel, Odijk, Daan |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: To be published in NeurIPS 2024 Workshop on Creativity & Generative AI. 7 pages, 3 figures |
| Databáze: | arXiv |
| Externí odkaz: |
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