DYPLODOC: Dynamic Plots for Document Classification
arXiv:2107.12226 · doi:10.3233/FAIA210283
Abstract
Narrative generation and analysis are still on the fringe of modern natural language processing yet are crucial in a variety of applications. This paper proposes a feature extraction method for plot dynamics. We present a dataset that consists of the plot descriptions for thirteen thousand TV shows alongside meta-information on their genres and dynamic plots extracted from them. We validate the proposed tool for plot dynamics extraction and discuss possible applications of this method to the tasks of narrative analysis and generation.
References in corpus (7)
- Deep Reinforcement Learning for Dialogue Generation
- Chinese Poetry Generation with Planning based Neural Network
- Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
- M2D: Monolog to Dialog Generation for Conversational Story Telling
- Story Realization: Expanding Plot Events into Sentences
- Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
- Predicting Movie Genres Based on Plot Summaries