$\text{COSMIC}$: Mutual Information for Task-Agnostic Summarization Evaluation

Abstract

Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that assesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probability of these tasks and the mutual information between source texts and generated summaries. We introduce $\text{COSMIC}$ as a practical implementation of this metric, demonstrating its strong correlation with human judgment-based metrics and its effectiveness in predicting downstream task performance. Comparative analyses against established metrics like $\text{BERTScore}$ and $\text{ROUGE}$ highlight the competitive performance of $\text{COSMIC}$.

ACL 2024