Most general-purpose extractive summarization models are trained on news
articles, which are short and present all important information upfront. As a
result, such models are biased on position and often perform a smart selection
of sentences from the beginning of the document. When summarizing long
narratives, which have complex structure and present information piecemeal,
simple position heuristics are not sufficient. In this paper, we propose to
explicitly incorporate the underlying structure of narratives into general
unsupervised and supervised extractive summarization models. We formalize
narrative structure in terms of key narrative events (turning points) and treat
it as latent in order to summarize screenplays (i.e., extract an optimal
sequence of scenes). Experimental results on the CSI corpus of TV screenplays,
which we augment with scene-level summarization labels, show that latent
turning points correlate with important aspects of a CSI episode and improve
summarization performance over general extractive algorithms leading to more
complete and diverse summaries.

本研究针对长篇叙述性文章提出了一种基于叙述结构的提取式摘要模型。通过将叙述结构转化为关键节点，导入非监督和监督提取式摘要模型，实验结果表明，潜在节点与 CSI 剧集的重要方面相关，并通过提高提取式算法的完整性和多样性改善了摘要性能。