  Keyword extraction is a fundamental task in natural language processing that
facilitates mapping of documents to a concise set of representative single and
multi-word phrases. Keywords from text documents are primarily extracted using
supervised and unsupervised approaches. In this paper, we present an
unsupervised technique that uses a combination of theme-weighted personalized
PageRank algorithm and neural phrase embeddings for extracting and ranking
keywords. We also introduce an efficient way of processing text documents and
training phrase embeddings using existing techniques. We share an evaluation
dataset derived from an existing dataset that is used for choosing the
underlying embedding model. The evaluations for ranked keyword extraction are
performed on two benchmark datasets comprising of short abstracts (Inspec), and
long scientific papers (SemEval 2010), and is shown to produce results better
than the state-of-the-art systems.
