  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.
