exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem

TL;DR

This paper presents exHarmony, a new benchmark for reviewer assignment using scholarly data, framing the problem as a retrieval task and evaluating various embedding-based methods to improve reviewer matching.

Contribution

It introduces a novel benchmark dataset for reviewer assignment, leveraging extensive scholarly data and diverse signals, and evaluates multiple retrieval methods for improved matching accuracy.

Findings

Contextualized embeddings outperform traditional methods.

Traditional lexical matching performs reasonably well.

The benchmark enables standardized evaluation of reviewer assignment methods.

Abstract

The peer review process is crucial for ensuring the quality and reliability of scholarly work, yet assigning suitable reviewers remains a significant challenge. Traditional manual methods are labor-intensive and often ineffective, leading to nonconstructive or biased reviews. This paper introduces the exHarmony (eHarmony but for connecting experts to manuscripts) benchmark, designed to address these challenges by re-imagining the Reviewer Assignment Problem (RAP) as a retrieval task. Utilizing the extensive data from OpenAlex, we propose a novel approach that considers a host of signals from the authors, most similar experts, and the citation relations as potential indicators for a suitable reviewer for a manuscript. This approach allows us to develop a standard benchmark dataset for evaluating the reviewer assignment problem without needing explicit labels. We benchmark various methods, including traditional lexical matching, static neural embeddings, and contextualized neural embeddings, and introduce evaluation metrics that assess both relevance and diversity in the context of RAP. Our results indicate that while traditional methods perform reasonably well, contextualized embeddings trained on scholarly literature show the best performance. The findings underscore the importance of further research to enhance the diversity and effectiveness of reviewer assignments.