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.

翻译：关键词抽取是自然语言处理中的基础性任务，通常包含两个主要部分：候选关键词抽取和关键词重要性估计。从人类理解文档的角度来看，我们通常根据短语的句法准确性、信息显著性和概念一致性来同时衡量其重要性。然而，现有的大多数关键词抽取方法仅关注其中部分方面，导致结果存在偏差。本文提出了一种从多视角估计关键词重要性的新方法（称为\textit{KIEMP}），以进一步优化关键词抽取的性能。具体而言，\textit{KIEMP}通过三个模块估计短语重要性：组块分析模块衡量其句法准确性，排序模块检验其信息显著性，匹配模块判断短语与整篇文档的概念（即主题）一致性。这三个模块通过端到端的多任务学习模型无缝衔接，有助于三者相互增强并平衡三个视角的影响。在六个基准数据集上的实验结果表明，\textit{KIEMP}在大多数情况下优于现有最优的关键词抽取方法。