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Event extraction has gained considerable interest due to its wide-rangingapplications. However, recent studies draw attention to evaluation issues,suggesting that reported scores may not accurately reflect the trueperformance. In this work, we identify and address evaluation challenges,including inconsistency due to varying data assumptions or preprocessing steps,the insufficiency of current evaluation frameworks that may introduce datasetor data split bias, and the low reproducibility of some previous approaches. Toaddress these challenges, we present TextEE, a standardized, fair, andreproducible benchmark for event extraction. TextEE comprises standardized datapreprocessing scripts and splits for 16 datasets spanning eight diverse domainsand includes 14 recent methodologies, conducting a comprehensive benchmarkreevaluation. We also evaluate five varied large language models on our TextEEbenchmark and demonstrate how they struggle to achieve satisfactoryperformance. Inspired by our reevaluation results and findings, we discuss therole of event extraction in the current NLP era, as well as future challengesand insights derived from TextEE. We believe TextEE, the first standardizedcomprehensive benchmarking tool, will significantly facilitate future eventextraction research.

TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction