SummScreen：一个用于抽象剧本摘要的数据集
SummScreen: A Dataset for Abstractive Screenplay Summarization
摘要：
我们介绍SummScreen，一个摘要数据集，由成对的电视剧成绩单和人类书面摘要组成。该数据集提供了一个具有挑战性的测试平台抽象总结有几个原因。情节细节通常在人物对话中间接表达，并可能分散在整个记录中。这些细节必须被发现和整合，以形成简洁的情节描述在重述。此外，电视剧本包含的内容并不直接与中心情节有关，而是用来发展人物或提供喜剧效果。这些信息很少包含在摘要中。由于人物是电视剧的基础，我们还提出了两个实体为中心的评价指标。从经验上讲，我们通过评估几种方法来表征数据集，包括神经模型和基于最近邻的方法。根据自动度量，Oracle提取方法优于所有基准模型，表明神经模型无法完全利用输入转录本。人的评价和定性分析表明，我们的非甲骨文模型与他们的甲骨文同行在产生忠实的情节事件方面的竞争力，可以受益于更好的内容选择器。预言和非预言模型都产生了不忠实的事实，并提出了未来的研究方向。
英文摘要：
We introduce SummScreen, a summarization dataset comprised of pairs of TV series transcripts and human written recaps. The dataset provides a challenging testbed for abstractive summarization for several reasons. Plot details are often expressed indirectly in character dialogues and may be scattered across the entirety of the transcript. These details must be found and integrated to form the succinct plot descriptions in the recaps. Also, TV scripts contain content that does not directly pertain to the central plot but rather serves to develop characters or provide comic relief. This information is rarely contained in recaps. Since characters are fundamental to TV series, we also propose two entity-centric evaluation metrics. Empirically, we characterize the dataset by evaluating several methods, including neural models and those based on nearest neighbors. An oracle extractive approach outperforms all benchmarked models according to automatic metrics, showing that the neural models are unable to fully exploit the input transcripts. Human evaluation and qualitative analysis reveal that our non-oracle models are competitive with their oracle counterparts in terms of generating faithful plot events and can benefit from better content selectors. Both oracle and non-oracle models generate unfaithful facts, suggesting future research directions.
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。
