  Natural language processing techniques have demonstrated promising results in
keyphrase generation. However, one of the major challenges in \emph{neural}
keyphrase generation is processing long documents using deep neural networks.
Generally, documents are truncated before given as inputs to neural networks.
Consequently, the models may miss essential points conveyed in the target
document. To overcome this limitation, we propose \emph{SEG-Net}, a neural
keyphrase generation model that is composed of two major components, (1) a
selector that selects the salient sentences in a document and (2) an
extractor-generator that jointly extracts and generates keyphrases from the
selected sentences. SEG-Net uses Transformer, a self-attentive architecture, as
the basic building block with a novel \emph{layer-wise} coverage attention to
summarize most of the points discussed in the document. The experimental
results on seven keyphrase generation benchmarks from scientific and web
documents demonstrate that SEG-Net outperforms the state-of-the-art neural
generative methods by a large margin.
