Toward a Computational Framework of Suspense and Dramatic Arc

We propose a computational framework for the recognition of suspense and dramatic arc in stories. Suspense is an affective response to narrative structure that accompanies the reduction in quantity or quality of plans available to a protagonist faced with potential goal failure and/or harm. Our work is motivated by the recognition that computational systems are historically unable to reliably reason about aesthetic or affective qualities of story structures. Our proposed framework, Dramatis, reads a story, identifies potential failures in the plans and goals of the protagonist, and computes a suspense rating at various points in the story. To compute suspense, Dramatis searches for ways in which the protagonist can overcome the failure and produces a rating inversely proportional to the likelihood of the best approach to overcoming the failure. If applied to story generation, Dramatis could allow for the creation of stories with knowledge of suspense and dramatic arc.