$\texttt{COSMIC}$: Mutual Information for Task-Agnostic Summarization Evaluation
This provides a more meaningful evaluation metric for summarization systems, though it is incremental as it builds on existing mutual information concepts.
The paper tackles the challenge of evaluating summarizers by proposing a task-oriented approach that measures how well summaries preserve information for downstream tasks, showing that COSMIC correlates strongly with human judgment and predicts task performance effectively.
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 $\texttt{COSMIC}$ 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 $\texttt{BERTScore}$ and $\texttt{ROUGE}$ highlight the competitive performance of $\texttt{COSMIC}$.