Narratives are a ubiquitous language phenomenon found in every society and culture, and are used by nearly every person in the world every single day.  Perhaps the single most important feature that distinguishes narrative from other forms of discourse is their structure: the structure of a narrative communicates its meaning and purpose and gives rise to numerous cognitive benefits that improve comprehension, retention, understanding, and use of information given in narrative form. I present results from the Cognac Lab at FIU on automatically extracting narrative structure using approaches drawn from machine learning and computational linguistics. First, I present experiments that show that a classic theory of narrative structure (Vladimir Propp's morphology of the folktale) can be reliably reproduced by people.  Second, I demonstrate a specially-designed learning algorithm can learn Propp’s theory from raw data.  Finally, I outline the most recent results from student work in the lab, including algorithms for story detection, character classification, and motif extraction, which point the way forward to systems for fully automatic narrative structure extraction and a major advance in machine intelligence.