paper

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

arXiv:1907.06458 · doi:10.1007/978-3-030-31372-2_26

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.

arXiv commentThe final authenticated publication is available online at https://doi.org/10.1007/978-3-030-31372-2_26

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