An empirical evaluation of document embeddings and similarity metrics for scientific articles

Abstract

The comparison of documents—such as articles or patents search, bibliography recommendations systems, visualization of document collections, etc.—has a wide range of applications in several fields. One of the key tasks that such problems have in common is the evaluation of a similarity metric. Many such metrics have been proposed in the literature. Lately, deep learning techniques have gained a lot of popularity. However, it is difficult to analyze how those metrics perform against each other. In this paper, we present a systematic empirical evaluation of several of the most popular similarity metrics when applied to research articles. We analyze the results of those metrics in two ways, with a synthetic test that uses scientific papers and Ph.D. theses, and in a real-world scenario where we evaluate their ability to cluster papers from different areas of research.This research was funded by Project TIN2017-88515-C2-1-R funded by Ministerio de Economía y Competitividad, under MCIN/AEI/10.13039/501100011033/FEDER “A way to make Europe”.Peer ReviewedPostprint (published version

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