Generating different story tellings from semantic representations of narrative
Elena Rishes, Stephanie M. Lukin, David K. Elson, Marilyn A. Walker · 2013
Abstract. In order to tell stories in different voices for different audi-ences, interactive story systems require: (1) a semantic representation of story structure, and (2) the ability to automatically generate story and dialogue from this semantic representation using some form of Natural Language Generation (nlg). However, there has been limited research on methods for linking story structures to narrative descriptions of scenes and story events. In this paper we present an automatic method for converting from Scheherazade’s story intention graph, a semantic representation, to the input required by the personage nlg engine. Using 36 Aesop Fables distributed in DramaBank, a collection of story encodings, we train translation rules on one story and then test these rules by generating text for the remaining 35. The results are measured in terms of the string similarity metrics Levenshtein Distance and BLEU score. The results show that we can generate the 35 stories with cor-rect content: the test set stories on average are close to the output of the Scheherazade realizer, which was customized to this semantic rep-resentation. We provide some examples of story variations generated by personage. In future work, we will experiment with measuring the qual-ity of the same stories generated in different voices, and with techniques for making storytelling interactive.