Cognitive models of discourse comprehension for narrative generation

This article presents an approach to using cognitive models of narrative discourse comprehension to define an explicit computational model of a reader’s comprehension process during reading, predicting aspects of narrative focus and inferencing with precision. This computational model is employed in a narrative discourse generation system to select and sequence content from a partial plan representing story world facts, objects, and events, creating discourses that satisfy comprehension criteria. Cognitive theories of narrative discourse comprehension define explicit models of a reader’s mental state during reading. These cognitive models are created to test hypotheses and explain empirical results about reader comprehension, but do not often contain sufficient precision for implementation on a computer. Therefore, they have not previously been suitable for computational narrative generation. The results of three experiments are presented and discussed, exhibiting empirical support for the approach presented. This work makes a number of contributions that advance the state-of-the-art in narrative discourse generation: a formal model of narrative focus, a formal model of online inferencing in narrative, a method of selecting narrative discourse content to satisfy comprehension criteria, and both implementation and evaluation of these models.