Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID

TL;DR

This paper overviews the TREC-COVID challenge, an IR shared task evaluating search methods on COVID-19 scientific literature across five rounds with extensive participation, providing insights into system performance and lessons learned.

Contribution

It introduces the TREC-COVID benchmark dataset and evaluates IR methods for pandemic-related scientific literature, highlighting challenges and best practices.

Findings

Top systems achieved significant improvements over baseline methods.

Emerging topics required adaptive search strategies.

Lessons learned inform future pandemic-related IR research.

Abstract

We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19. The challenge was conducted over five rounds from April to July, 2020, with participation from 92 unique teams and 556 individual submissions. A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic. This paper provides a comprehensive overview of the structure and results of TREC-COVID. Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.