LMRank: Utilizing Pre-Trained Language Models and Dependency Parsing for Keyphrase Extraction

Keyphrase extraction is a Natural Language Processing task pertaining to the automatic extraction of salient terms that semantically encapsulate the major theme and topics of a document. In this article, we present LMRank, a novel approach that utilizes dependency parsing and the sentence embeddings of pre-trained language models to improve the accuracy of the keyphrase extraction task. In addition, we conduct a benchmark analysis of our approach, which showcases that it scales far better than similar ones in terms of execution time. The contribution of this work is threefold: (i) we propose a novel approach that significantly outperforms the state-of-the-art keyphrase extraction approaches in terms of time performance and accuracy in selected datasets; (ii) we provide a comparative evaluation of our approach against previous ones, by utilizing broadly used datasets in the literature and established evaluation metrics (e.g., the F1 and pF1 scores); (iii) we make the datasets and code used in our experiments public, aiming to further increase the reproducibility of this work and facilitate future research in the field.