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Keyword extraction is an important document process that aims at finding asmall set of terms that concisely describe a document's topics. The mostpopular state-of-the-art unsupervised approaches belong to the family of thegraph-based methods that build a graph-of-words and use various centralitymeasures to score the nodes (candidate keywords). In this work, we follow adifferent path to detect the keywords from a text document by modeling the maindistribution of the document's words using local word vector representations.Then, we rank the candidates based on their position in the text and thedistance between the corresponding local vectors and the main distribution'scenter. We confirm the high performance of our approach compared to strongbaselines and state-of-the-art unsupervised keyword extraction methods, throughan extended experimental study, investigating the properties of the localrepresentations.

Keywords lie far from the mean of all words in local vector space