Unsupervised Keyphrase Extraction from Scientific Publications

TL;DR

This paper introduces an unsupervised method for extracting keyphrases from scientific texts by identifying outlier words in semantic space, outperforming existing techniques.

Contribution

It presents a novel outlier detection-based approach for keyphrase extraction that leverages word embeddings and robust statistical modeling.

Findings

Outperforms state-of-the-art unsupervised methods

Effective in identifying relevant keyphrases

Utilizes outlier detection for semantic filtering

Abstract

We propose a novel unsupervised keyphrase extraction approach that filters candidate keywords using outlier detection. It starts by training word embeddings on the target document to capture semantic regularities among the words. It then uses the minimum covariance determinant estimator to model the distribution of non-keyphrase word vectors, under the assumption that these vectors come from the same distribution, indicative of their irrelevance to the semantics expressed by the dimensions of the learned vector representation. Candidate keyphrases only consist of words that are detected as outliers of this dominant distribution. Empirical results show that our approach outperforms state-of-the-art and recent unsupervised keyphrase extraction methods.