Abstract
The 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 a suitable knowledge without spending too much time and 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 ranking algorithm, called PhaseRank, that involves two phases on ranking candidate keyphrases. Experiment results on a real-world dataset of MOOC video lectures show that our proposed approach outperforms the state-of-the-art methods by 16% or more in terms of F-1 score.