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

Despite the significant advancements in keyphrase extraction and keyphrase generation methods, the predominant approach for evaluation only relies on exact matching with human references and disregards reference-free attributes. This scheme fails to recognize systems that generate keyphrases semantically equivalent to the references or diverse keyphrases that carry practical utility. To better assess the capability of keyphrase systems, we propose KPEval, a comprehensive evaluation framework consisting of four critical dimensions: 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 correlates better with human preferences compared to a range of previously used metrics. Using this framework, we re-evaluate 20 keyphrase systems and further discover that (1) the best model differs depending on the evaluation dimension; (2) the utility in downstream tasks does not always correlate with reference-based metrics; and (3) large language models like GPT-3.5 exhibit a strong performance under reference-free evaluation.