Better representations of plot structure could greatly improve computational methods for summarizing and generating stories. Current representations lack abstraction, focusing too closely on events. We present a kernel for comparing novelistic plots at a higher level, in terms of the cast of characters they depict and the social relationships between them. Our kernel compares the characters of different novels to one another by measuring their frequency of occurrence over time and the descriptive and emotional language associated with them. Given a corpus of 19thcentury novels as training data, our method can accurately distinguish held-out novels in their original form.
