  Efficiently identifying keyphrases that represent a given document is a
challenging task. In the last years, plethora of keyword detection approaches
were proposed. These approaches can be based on statistical (frequency-based)
properties of e.g., tokens, specialized neural language models, or a
graph-based structure derived from a given document. The graph-based methods
can be computationally amongst the most efficient ones, while maintaining the
retrieval performance. One of the main properties, common to graph-based
methods, is their immediate conversion of token space into graphs, followed by
subsequent processing. In this paper, we explore a novel unsupervised approach
which merges parts of a document in sequential form, prior to construction of
the token graph. Further, by leveraging personalized PageRank, which considers
frequencies of such sub-phrases alongside token lengths during node ranking, we
demonstrate state-of-the-art retrieval capabilities while being up to two
orders of magnitude faster than current state-of-the-art unsupervised detectors
such as YAKE and MultiPartiteRank. The proposed method's scalability was also
demonstrated by computing keyphrases for a biomedical corpus comprised of 14
million documents in less than a minute.
