Narrative Complexity Based on Summarization Algorithms

Abstract

Narrative structures can only be defined in terms of some internal memory representation, but narrative complexity is more properly characterized by information processing requirements. Story grammars, plan and goal hierarchies, and causal chain representations all provide a sense of structure which is largely removed from the processes that produce or access that memory representation. In this paper we introduce the notion of algorithmic equivalence as a means of generating more algorithmically-oriented taxonomies for memory representations. Using memory representations based on plot units, we define two narratives to be algorithmically equivalent if they can be effectively summarized by the same retrieval process. This perspective on representational strategies is an especially natural one from a processing point of view, since the computational complexity of a particular information processing task must be measured in terms of the algorithms involved.