Cognitive models of discourse comprehension for narrative generation

Recent work in the area of narrative generation has sought to develop systems that automatically produce experiences for a user that are understood as stories. Much of this prior work, however, has focused on the structural aspects of narrative rather than the process of narrative comprehension undertaken by readers. 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 the comprehension processes of readers. They do not often contain sufficient precision for implementation on a computer, and thus, they are not yet suitable for computational generation purposes. This dissertation employs 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 content from an event log, creating discourses that satisfy comprehension criteria. The results of three experiments are presented and discussed, exhibiting empirical support for the computational reader model and the results of generation. This dissertation 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 implementation and evaluation of these models.