  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.

Computation and Language