Are Key-Phrases All That Reviewers Care About? A Comprehensive Benchmarking of Reviewer Matchmaking Systems

Reviewer Matchmaking (RM) is a pivotal process in academic publishing that aligns manuscripts with appropriate reviewers based on their expertise and prior publications. The demand for an automated RM system has escalated with the significant surge in submissions over the past decade. State-of-the-art (SOTA) RM models are document-representation-based (DR-RM) and match the manuscript and reviewer's past publication using a similarity method defined on a high-dimensional vector space. However, they are far from accurate despite their large-scale usage. In this paper, we establish that conventional RM evaluation measures are unreliable and instead emphasize that standard correlation measures are adequate. For the first time, we compare the performance of six SOTA DR-RM models with those of fourteen SOTA Key-phrase Extraction-based RM (KPE-RM) models - an alternate unexplored approach. We observe that KPE-RM models show comparable results in many cases, with the new best model being PatternRank-RM - a KPE-RM model beating the best DR-RM model SPECTER2-RM (Pearson: 0.004+, Spearman: 0.006+, Kendall: 0.043+). We conclude that KPE-RM models must be contextualized to the RM task and cannot be used as plug-n-play.