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.
@article{arxiv.2210.14174,
title = {Towards Interpretable Summary Evaluation via Allocation of Contextual Embeddings to Reference Text Topics},
author = {Ben Schaper and Christopher Lohse and Marcell Streile and Andrea Giovannini and Richard Osuala},
journal= {arXiv preprint arXiv:2210.14174},
year = {2022}
}