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Despite the significant advancements in keyphrase extraction and keyphrasegeneration methods, the predominant approach for evaluation only relies onexact matching with human references and disregards reference-free attributes.This scheme fails to recognize systems that generate keyphrases semanticallyequivalent to the references or diverse keyphrases that carry practicalutility. To better assess the capability of keyphrase systems, we proposeKPEval, a comprehensive evaluation framework consisting of four criticaldimensions: saliency, faithfulness, diversity, and utility. For each dimension,we design semantic-based metrics that align with the evaluation objectives.Meta-evaluation studies demonstrate that our evaluation strategy correlatesbetter with human preferences compared to a range of previously used metrics.Using this framework, we re-evaluate 20 keyphrase systems and further discoverthat (1) the best model differs depending on the evaluation dimension; (2) theutility in downstream tasks does not always correlate with reference-basedmetrics; and (3) large language models like GPT-3.5 exhibit a strongperformance under reference-free evaluation.

KPEval: Towards Fine-grained Semantic-based Evaluation of Keyphrase Extraction and Generation Systems