Multi-view Story Characterization from Movie Plot Synopses and Reviews
This work addresses the challenge of automated story analysis for applications in media and entertainment, though it is incremental as it builds on existing multi-view and attention techniques.
The paper tackles the problem of characterizing stories by inferring properties like theme and style from movie plot synopses and reviews, using a multi-view model that improves over synopsis-only methods by incorporating reviews to extract complementary attributes without direct supervision.
This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how can we take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/ multiview-tag-2020.