FFCI: A Framework for Interpretable Automatic Evaluation of Summarization

• URL: http://arxiv.org/abs/2011.13662v3
• Date: Mon, 28 Feb 2022 02:03:28 GMT
• Title: FFCI: A Framework for Interpretable Automatic Evaluation of Summarization
• Authors: Fajri Koto and Timothy Baldwin and Jey Han Lau
• Abstract summary: We propose FFCI, a framework for fine-grained summarization evaluation. We construct a novel dataset for focus, coverage, and inter-sentential coherence. We apply the developed metrics in evaluating a broad range of summarization models across two datasets.
• Score: 43.375797352517765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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