This paper creates a paradigm shift with regard to the way we build neural
extractive summarization systems. Instead of following the commonly used
framework of extracting sentences individually and modeling the relationship
between sentences, we formulate the extractive summarization task as a semantic
text matching problem, in which a source document and candidate summaries will
be (extracted from the original text) matched in a semantic space. Notably,
this paradigm shift to semantic matching framework is well-grounded in our
comprehensive analysis of the inherent gap between sentence-level and
summary-level extractors based on the property of the dataset.
Besides, even instantiating the framework with a simple form of a matching
model, we have driven the state-of-the-art extractive result on CNN/DailyMail
to a new level (44.41 in ROUGE-1). Experiments on the other five datasets also
show the effectiveness of the matching framework. We believe the power of this
matching-based summarization framework has not been fully exploited. To
encourage more instantiations in the future, we have released our codes,
processed dataset, as well as generated summaries in
this https URL

通过语义文本匹配任务范式，本论文提出了一种新的神经抽取式摘要系统构建方法。经过与原有抽取式摘要方法数据集的实验对比，该方法取得了更好的抽取结果，并通过对 CNN / DailyMail 数据集的实验，创造了 44.41 的新高水平。我们相信，这种基于匹配的摘要框架的潜力还未完全被利用。