Research Area:  Machine Learning

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. In this paper, we present Pattern Rank, which leverages pretrained language models and part-of-speech for unsupervised keyphrase extraction from single documents. Our experiments show Pattern Rank achieves higher precision, recall and F1-scores than previous state-of-the-art approaches. In addition, we present the Keyphrase Vectorizers package, which allows easy modification of part-of-speech patterns for candidate keyphrase selection, and hence adaptation of our approach to any domain.

Keywords:  
Leveraging Pretrained Language Models
Unsupervised
Keyphrase Extraction
Patterns
Machine Learning
Deep Learning

Author(s) Name:  Tim Schopf, Simon Klimek, Florian Matthes

Publisher name:  arXiv:2210.05245

DOI:  10.48550/arXiv.2210.05245

Volume Information:  

Paper Link:   https://arxiv.org/abs/2210.05245