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Keyword extraction is a fundamental task in natural language processing thatfacilitates mapping of documents to a concise set of representative single andmulti-word phrases. Keywords from text documents are primarily extracted usingsupervised and unsupervised approaches. In this paper, we present anunsupervised technique that uses a combination of theme-weighted personalizedPageRank algorithm and neural phrase embeddings for extracting and rankingkeywords. We also introduce an efficient way of processing text documents andtraining phrase embeddings using existing techniques. We share an evaluationdataset derived from an existing dataset that is used for choosing theunderlying embedding model. The evaluations for ranked keyword extraction areperformed on two benchmark datasets comprising of short abstracts (Inspec), andlong scientific papers (SemEval 2010), and is shown to produce results betterthan the state-of-the-art systems.

Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddings