Entity Commonsense Representation for Neural Abstractive Summarization

Amplayo, Reinald Kim; Lim, Seonjae; Hwang, Seung-won

doi:10.48550/arxiv.1806.05504

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FFCI: A Framework for Interpretable Automatic Evaluation of Summarization

Koto

Lau²,

Baldwin³

2022

jair

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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.

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