Narrative Complexity Based on Summarization Algorithms

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 a l l provide a sense of structure which is largely removed from the processes that produce or access that memory representation. In th is paper we introduce the notion of algorithmic equivalence as a means of generating more algori thmical ly-oriented taxonomies for memory representations. Using memory representations based on plot un i ts , we define two narratives to be algori thmical ly equivalent if they can be ef fect ive ly summarized by the same re t r ieva l process. This perspective on representational strategies is an especially natural one from a processing point of view, since the computational complexity of a part icular information processing task must be measured in terms of the algorithms involved .