Rachelyn farrell

Research Scientist
Narrative Intelligence Lab
University of Kentucky
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Salience Vectors for Measuring Distance between Stories

Narrative planners generate sequences of actions that represent story plots given a story domain model. This is a useful way to create branching stories for interactive narrative systems that maintain logical consistency across multiple storylines with different content. There is a need for story comparison techniques that can enable systems like experience managers and domain authoring tools to reason about similarities and differences between multiple stories or branches. We present an algorithm for summarizing narrative plans as numeric vectors based on a cognitive model of human story perception. The vectors encode important story information and can be compared using standard distance functions to quantify the overall semantic difference between two stories. We show that this distance metric is highly accurate based on human annotations of story similarity, and compare it to several alternative approaches. We also explore variations of our method in an attempt to broaden its applicability to other types of story systems.

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Our dataset of human assessments of story similarity is archived here!


Rachelyn Farrell, Mira Fisher, Stephen G. Ware. Salience vectors for measuring distance between stories. In Proceedings of the 18th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 95-104, 2022.