论文标题
与内容选择和融合的神经抽象摘要的级联方法
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
论文作者
论文摘要
我们提出了一项实证研究，支持级联架构来进行神经文本摘要。摘要实践差异很大，但除了新闻摘要以外，很少有其他方法可以提供足够数量的培训数据，以满足端到端神经抽象系统的要求，这些神经抽象系统可以共同执行内容选择和表面实现以产生摘要。这样的系统也对汇总评估构成了挑战，因为它们迫使内容选择与文本生成一起评估，但是对后者的评估仍然是一个未解决的问题。在本文中，我们提出了经验结果表明，级联管道的性能分别识别重要的内容并将它们拼接在一起成连贯的文本，与端到端系统的相当或超出距离，而管道架构则可以选择灵活的内容选择。我们终于讨论了如何在神经文本摘要中利用级联管道，并阐明重要的方向以进行未来的研究。
We present an empirical study in favor of a cascade architecture to neural text summarization. Summarization practices vary widely but few other than news summarization can provide a sufficient amount of training data enough to meet the requirement of end-to-end neural abstractive systems which perform content selection and surface realization jointly to generate abstracts. Such systems also pose a challenge to summarization evaluation, as they force content selection to be evaluated along with text generation, yet evaluation of the latter remains an unsolved problem. In this paper, we present empirical results showing that the performance of a cascaded pipeline that separately identifies important content pieces and stitches them together into a coherent text is comparable to or outranks that of end-to-end systems, whereas a pipeline architecture allows for flexible content selection. We finally discuss how we can take advantage of a cascaded pipeline in neural text summarization and shed light on important directions for future research.
