Despite extensive recent advances in summary generation models, evaluation of
auto-generated summaries still widely relies on single-score systems
insufficient for transparent assessment and in-depth qualitative analysis.
Towards bridging this gap, we propose the multifaceted interpretable summary
evaluation method (MISEM), which is based on allocation of a summary's
contextual token embeddings to semantic topics identified in the reference
text. We further contribute an interpretability toolbox for automated summary
evaluation and interactive visual analysis of summary scoring, topic
identification, and token-topic allocation. MISEM achieves a promising .404
Pearson correlation with human judgment on the TAC'08 dataset.

本文提出了一种基于多方面可解释性的自动摘要评估方法（MISEM），包括语义主题识别和交互式可视化分析工具。该方法针对自动生成的摘要进行评估，达到了.404 的人类判断相关性，能够通过单一评分系统难以实现的透明评估和深度质量分析。