  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.
