Published on Mon Jul 15 2019

RaKUn: Rank-based Keyword extraction via Unsupervised learning and Meta vertex aggregation

Blaž Škrlj, Andraž Repar, Senja Pollak

Keyword extraction is used for summarizing the content of a document. We explore how a graph-theoreticmeasure applied to graphs derived from a given text can be used to efficientlyidentify and rank keywords. The proposed method is unsupervised and interpretable.

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Abstract

Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems. We explore how load centrality, a graph-theoretic measure applied to graphs derived from a given text can be used to efficiently identify and rank keywords. Introducing meta vertices (aggregates of existing vertices) and systematic redundancy filters, the proposed method performs on par with state-of-the-art for the keyword extraction task on 14 diverse datasets. The proposed method is unsupervised, interpretable and can also be used for document visualization.