Massive open online courses (MOOCs) have emerged as a great resource for learners. Numerous challenges remain to be addressed in order to make MOOCs more useful and convenient for learners. One such challenge is how to automatically extract a set of keyphrases from MOOC video lectures that can help students quickly identify the right knowledge they want to learn and thus expedite their learning process. In this paper, we propose SemKeyphrase, an unsupervised cluster-based approach for keyphrase extraction from MOOC video lectures. SemKeyphrase incorporates a new semantic relatedness metric and a ranking algorithm, called PhraseRank, that involves two phases on ranking candidates. We conducted experiments on a real-world dataset of MOOC video lectures, and the results show that our proposed approach outperforms the state-of-the-art keyphrase extraction methods.

大规模的在线公开课程（MOOC）已成为学习者的重要资源。为了使MOOC对学习者更加有用和方便，仍然有许多挑战需要解决。这样的挑战之一就是如何从MOOC视频讲座中自动提取一组关键短语，以帮助学生快速识别他们想学习的正确知识，从而加快他们的学习过程。在本文中，我们提出了SemKeyphrase，这是一种无监督的，基于聚类的方法，用于从MOOC视频讲座中提取关键短语。SemKeyphrase合并了一个新的语义相关性度量标准和一种称为PhraseRank的排名算法，该算法涉及对候选排名进行两个阶段。我们在MOOC视频讲座的真实数据集上进行了实验，