  Keyphrase extraction is a fundamental task in Natural Language Processing,
which usually contains two main parts: candidate keyphrase extraction and
keyphrase importance estimation. From the view of human understanding
documents, we typically measure the importance of phrase according to its
syntactic accuracy, information saliency, and concept consistency
simultaneously. However, most existing keyphrase extraction approaches only
focus on the part of them, which leads to biased results. In this paper, we
propose a new approach to estimate the importance of keyphrase from multiple
perspectives (called as \textit{KIEMP}) and further improve the performance of
keyphrase extraction. Specifically, \textit{KIEMP} estimates the importance of
phrase with three modules: a chunking module to measure its syntactic accuracy,
a ranking module to check its information saliency, and a matching module to
judge the concept (i.e., topic) consistency between phrase and the whole
document. These three modules are seamlessly jointed together via an end-to-end
multi-task learning model, which is helpful for three parts to enhance each
other and balance the effects of three perspectives. Experimental results on
six benchmark datasets show that \textit{KIEMP} outperforms the existing
state-of-the-art keyphrase extraction approaches in most cases.
