QuestEval: Summarization Asks for Fact-based Evaluation

Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate this issue, recent work has proposed evaluation metrics which rely on question answering models to assess whether a summary contains all the relevant information in its source document. Though promising, the proposed approaches have so far failed to correlate better than ROUGE with human judgments. In this paper, we extend previous approaches and propose a unified framework, named QU E S TEV A L. In contrast to established metrics such as ROUGE or BERTScore, QU E S TEV A L does not require any groundtruth reference. Nonetheless, QU E S TEV A L substantially improves the correlation with human judgments over four evaluation dimensions (consistency, coherence, fluency, and relevance), as shown in extensive experiments. We make code and models available. 1