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.

通过提取场景的显著性，针对电影剧本等长篇故事性文本进行抽象概括是具有挑战性的。本研究引入了一个包含 100 部电影人工标注显著场景的数据集，提出了一个两阶段的抽象概括方法，首先识别出剧本中的显著场景，然后利用这些场景生成摘要。基于问答的评估结果显示，我们的模型优于以往最先进的摘要方法，并且比将整个剧本作为输入的模型更准确地反映了电影的信息内容。