KPEval: Towards Fine-Grained Semantic-Based Keyphrase Evaluation
This addresses the need for more nuanced evaluation in NLP for researchers and practitioners, though it is incremental as it builds on prior metrics.
The paper tackles the problem of evaluating keyphrase extraction and generation systems by proposing KPEval, a framework with semantic-based metrics across four aspects, which correlates better with human preferences and reveals blind-spots in existing evaluations, such as underestimating large language models.
Despite the significant advancements in keyphrase extraction and keyphrase generation methods, the predominant approach for evaluation mainly relies on exact matching with human references. 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 aspects: reference agreement, faithfulness, diversity, and utility. For each aspect, we design semantic-based metrics to reflect the evaluation objectives. Meta-evaluation studies demonstrate that our evaluation strategy correlates better with human preferences compared to a range of previously proposed metrics. Using KPEval, we re-evaluate 23 keyphrase systems and discover that (1) established model comparison results have blind-spots especially when considering reference-free evaluation; (2) large language models are underestimated by prior evaluation works; and (3) there is no single best model that can excel in all the aspects.