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Scientific publishing heavily relies on the assessment of quality ofsubmitted manuscripts by peer reviewers. Assigning a set of matching reviewersto a submission is a highly complex task which can be performed only by domainexperts. We introduce RevASIDE, a reviewer recommendation system that assignssuitable sets of complementing reviewers from a predefined candidate poolwithout requiring manually defined reviewer profiles. Here, suitabilityincludes not only reviewers' expertise, but also their authority in the targetdomain, their diversity in their areas of expertise and experience, and theirinterest in the topics of the manuscript. We present three new data sets forthe expert search and reviewer set assignment tasks and compare the usefulnessof simple text similarity methods to document embeddings for expert search.Furthermore, an quantitative evaluation demonstrates significantly betterresults in reviewer set assignment compared to baselines. A qualitativeevaluation also shows their superior perceived quality.

RevASIDE: Assignment of Suitable Reviewer Sets for Publications from Fixed Candidate Pools