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Keyword extraction is used for summarizing the content of a document andsupports efficient document retrieval, and is as such an indispensable part ofmodern text-based systems. We explore how load centrality, a graph-theoreticmeasure applied to graphs derived from a given text can be used to efficientlyidentify and rank keywords. Introducing meta vertices (aggregates of existingvertices) and systematic redundancy filters, the proposed method performs onpar with state-of-the-art for the keyword extraction task on 14 diversedatasets. The proposed method is unsupervised, interpretable and can also beused for document visualization.

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