  Keyphrase extraction is the process of automatically selecting a small set of
most relevant phrases from a given text. Supervised keyphrase extraction
approaches need large amounts of labeled training data and perform poorly
outside the domain of the training data. In this paper, we present PatternRank,
which leverages pretrained language models and part-of-speech for unsupervised
keyphrase extraction from single documents. Our experiments show PatternRank
achieves higher precision, recall and F1-scores than previous state-of-the-art
approaches. In addition, we present the KeyphraseVectorizers package, which
allows easy modification of part-of-speech patterns for candidate keyphrase
selection, and hence adaptation of our approach to any domain.
