A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
This addresses the problem of limited training data and evaluation difficulties in summarization for researchers and practitioners, though it is incremental as it builds on existing pipeline methods.
The paper tackles the challenge of neural abstractive summarization by proposing a cascade architecture that separates content selection from text generation, showing it performs comparably or better than end-to-end systems while offering more flexibility.
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.