Unsupervised KeyPhrase Extraction using Graph and Embedding Techniques

Abstract

The process of extracting keyphrases from a document automatically, without any supervision, is referred to as Unsupervised Keyphrase Extraction. This method aims to produce a brief summary of the main content of the document. Embedding-based methods comprise computing similarity between candidate keyphrases and documents embeddings. In this paper, we find that filtering candidate keyphrases using graph-based techniques enriches frequent candidates which are reranked using embeddings. On comparing the proposed model to the current state-of-the-art unsupervised Keyphrase Extraction approaches across three KPE benchmarks, it was found that the proposed model outperformed them. © 2023 IEEE.