  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.
