  In this paper, we propose FFCI, a framework for fine-grained summarization
evaluation that comprises four elements: faithfulness (degree of factual
consistency with the source), focus (precision of summary content relative to
the reference), coverage (recall of summary content relative to the reference),
and inter-sentential coherence (document fluency between adjacent sentences).
We construct a novel dataset for focus, coverage, and inter-sentential
coherence, and develop automatic methods for evaluating each of the four
dimensions of FFCI based on cross-comparison of evaluation metrics and
model-based evaluation methods, including question answering (QA) approaches,
semantic textual similarity (STS), next-sentence prediction (NSP), and scores
derived from 19 pre-trained language models. We then apply the developed
metrics in evaluating a broad range of summarization models across two
datasets, with some surprising findings.
