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

Towards Interpretable Summary Evaluation via Allocation of Contextual Embeddings to Reference Text Topics