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.