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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. | Character-based Kernels for Novelistic Plot Structure Micha Elsner Institute for Language Cognition and Computation ILCC School of Informatics University of Edinburgh melsner0@gmail.com Abstract 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 19th-century novels as training data our method can accurately distinguish held-out novels in their original form from artificially disordered or reversed surrogates demonstrating its ability to robustly represent important aspects of plot structure. 1 Introduction Every culture has stories and storytelling is one of the key functions of human language. Yet while we have robust flexible models for the structure of informative documents for instance Chen et al. 2009 Abu Jbara and Radev 2011 current approaches have difficulty representing the narrative structure of fictional stories. This causes problems for any task requiring us to model fiction including summarization and generation of stories Kazantseva and Szpakowicz 2010 show that state-of-the-art summarizers perform extremely poorly on short fictional texts1 . A major problem with applying models for informative Apart from Kazantseva we know of one other attempt to apply a modern summarizer to fiction by the artist Jason Huff using Microsoft Word 2008 s extractive summary feature http jason-huff.com text to fiction is that the most important structure underlying the narrative its plot occurs at a high level of abstraction while the actual narration is of a series of .
