Select and Summarize: Scene Saliency for Movie Script Summarization
This addresses the challenge of summarizing long narrative texts like movie scripts for applications in entertainment or analysis, though it is incremental as it builds on existing summarization techniques with a new dataset and scene selection step.
The paper tackles abstractive summarization of movie scripts by identifying salient scenes, introducing a dataset of 100 movies with human-annotated scenes and a two-stage model that first selects these scenes before generating summaries. The model outperforms previous state-of-the-art methods in QA-based evaluation, reflecting movie information more accurately than using the entire script.
Abstractive summarization for long-form narrative texts such as movie scripts is challenging due to the computational and memory constraints of current language models. A movie script typically comprises a large number of scenes; however, only a fraction of these scenes are salient, i.e., important for understanding the overall narrative. The salience of a scene can be operationalized by considering it as salient if it is mentioned in the summary. Automatically identifying salient scenes is difficult due to the lack of suitable datasets. In this work, we introduce a scene saliency dataset that consists of human-annotated salient scenes for 100 movies. We propose a two-stage abstractive summarization approach which first identifies the salient scenes in script and then generates a summary using only those scenes. Using QA-based evaluation, we show that our model outperforms previous state-of-the-art summarization methods and reflects the information content of a movie more accurately than a model that takes the whole movie script as input.