Title
Leveraging Narrative to Generate Movie Script
Abstract
AbstractGenerating a text based on a predefined guideline is an interesting but challenging problem. A series of studies have been carried out in recent years. In dialogue systems, researchers have explored driving a dialogue based on a plan, while in story generation, a storyline has also been proved to be useful. In this article, we address a new task—generating movie scripts based on a predefined narrative. As an early exploration, we study this problem in a “retrieval-based” setting. We propose a model (ScriptWriter-CPre) to select the best response (i.e., next script line) among the candidates that fit the context (i.e., previous script lines) as well as the given narrative. Our model can keep track of what in the narrative has been said and what is to be said. Besides, it can also predict which part of the narrative should be paid more attention to when selecting the next line of script. In our study, we find the narrative plays a different role than the context. Therefore, different mechanisms are designed for deal with them. Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where end-users can upload their narratives freely when watching a movie. This new dataset is made available publicly to facilitate other studies in text generation under the guideline. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.
Year
DOI
Venue
2022
10.1145/3507356
ACM Transactions on Information Systems
Keywords
DocType
Volume
Narrative-guided Text Generation, Movie Script Generation, Retrieval-based Method
Journal
40
Issue
ISSN
Citations 
4
1046-8188
0
PageRank 
References 
Authors
0.34
13
6
Name
Order
Citations
PageRank
Y Zhu100.34
R Song200.34
Jian-yun Nie33681238.61
P Du400.34
Zhicheng Dou570641.96
Jing Zhou632754.75