PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction
Tim Schopf, Simon Klimek, Florian Matthes
Abstract
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 (Bennani-Smires et al., 2018).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 F 1 -scores than previous state-of-the-art approaches.In addition, we present the KeyphraseVectorizers a package, which allows easy modification of part-of-speech patterns for candidate keyphrase selection, and hence adaptation of our approach to any domain.