On Narrative Information and the Distillation of Stories

Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Jürgen Schmidhuber

Keywords: Artificial Neural Networks, Contrastive Learning, Evolutionary Algorithms, Information Theory, Narratives, Stories

TL;DR: We introduce narrative information and use contrastive learning to distill the narrative essence out of music albums.

Abstract: The act of telling stories is a fundamental part of what it means to be human. This work introduces the concept of narrative information, which we define to be the overlap in information space between a story and the items that compose the story. Using contrastive learning methods, we show how modern artificial neural networks can be leveraged to distill stories and extract a representation of the narrative information. We then demonstrate how evolutionary algorithms can leverage this to extract a set of narrative templates and how these templates—in tandem with a novel curve-fitting algorithm we introduce—can reorder music albums to automatically induce stories in them. In the process of doing so, we give strong statistical evidence that these narrative information templates are present in existing albums. While we experiment only with music albums here, the premises of our work extend to any form of (largely) independent media.

In-person Presentation: yes

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2211.12423/code)