选择与总结：用于电影剧本摘要的场景显著性
Select and Summarize: Scene Saliency for Movie Script Summarization
AI总结：
针对电影剧本长篇叙事摘要难题，本文构建含100部电影人工标注显著场景的数据集，并提出先筛选显著场景再生成摘要的两阶段方法，QA评估显示其优于现有方法。
摘要：
对电影剧本等长篇叙事文本进行生成式摘要颇具挑战，原因在于当前语言模型存在计算和内存限制。一部电影剧本通常包含大量场景，但其中只有一小部分场景是显著的，即对理解整体叙事很重要。场景的显著性可以通过如下方式操作化：如果某个场景在摘要中被提及，就将其视为显著。由于缺乏合适的数据集，自动识别显著场景十分困难。在本工作中，我们引入了一个场景显著性数据集，其中包含针对100部电影的人工标注显著场景。我们提出了一种两阶段生成式摘要方法，该方法首先识别剧本中的显著场景，然后仅使用这些场景生成摘要。通过基于QA的评估，我们表明我们的模型优于以往最先进的摘要方法，并且与将整部电影剧本作为输入的模型相比，能够更准确地反映电影的信息内容。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
