Story Embedding: Learning Distributed Representations of Stories Based on Character Networks (Extended Abstract)

Abstract

This study aims to represent stories in narrative works (i.e., creative works that contain stories) with a fixed-length vector. We apply subgraph-based graph embedding models to dynamic social networks of characters that appeared in stories (character networks). We suppose that interactions between characters reflect the content of stories. We discretize the interactions by discovering the subgraphs and learn representations of stories by predicting occurrences of the subgraphs in corresponding character networks. We find subgraphs rooted in each character on each scene in multiple scales, using the WL (Weisfeiler-Lehman) relabeling process. To predict occurrences of subgraphs, we apply two approaches: (i) considering changes in subgraphs according to scenes and (ii) focusing on subgraphs on the last scene. We evaluated the proposed models by measuring the similarity between real movies with vector representations that were generated by the models.