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FFCI: A Framework for Interpretable Automatic Evaluation of Summarization

2020-11-27 10:57:18
Fajri Koto, Jey Han Lau, Timothy Baldwin
arXiv_CL
                   
Abstract

In this paper, we propose FFCI, a framework for automatic summarization evaluation that comprises four elements: Faithfulness, Focus, Coverage, and Inter-sentential coherence. We design FFCI by comprehensively studying traditional evaluation metrics and model-based evaluations, including question answering (QA) approaches, STS, next-sentence prediction (NSP), and scores from 19 pre-trained language models. Our study reveals three key findings: (1) calculating BertSCORE between the summary and article sentences yields a higher correlation score than recently-proposed QA-based evaluation methods for faithfulness evaluation; (2) GPT2Score has the best Pearson's correlation for focus and coverage; and (3) a simple NSP model is effective at evaluating inter-sentential coherence.

Abstract (translated)

URL

https://arxiv.org/abs/2011.13662

PDF

https://arxiv.org/pdf/2011.13662.pdf

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