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arXiv · 2212.10013

DocAsRef: An Empirical Study on Repurposing Reference-Based Summary Quality Metrics Reference-Freely

Forrest Sheng Bao·Ruixuan Tu·Ge Luo·Yinfei Yang·Hebi Li·Minghui Qiu·Youbiao He·Cen Chen
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Abstract

Automated summary quality assessment falls into two categories: reference-based and reference-free. Reference-based metrics, historically deemed more accurate due to the additional information provided by human-written references, are limited by their reliance on human input. In this paper, we hypothesize that the comparison methodologies used by some reference-based metrics to evaluate a system summary against its corresponding reference can be effectively adapted to assess it against its source document, thereby transforming these metrics into reference-free ones. Experimental results support this hypothesis. After being repurposed reference-freely, the zero-shot BERTScore using the pretrained DeBERTa-large-MNLI model of <0.5B parameters consistently outperforms its original reference-based version across various aspects on the SummEval and Newsroom datasets. It also excels in comparison to most existing reference-free metrics and closely competes with zero-shot summary evaluators based on GPT-3.5.

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Forrest Sheng Bao, Ruixuan Tu, Ge Luo, Yinfei Yang, Hebi Li, Minghui Qiu, Youbiao He, Cen Chen. 2022-12-20. DocAsRef: An Empirical Study on Repurposing Reference-Based Summary Quality Metrics Reference-Freely. https://arxiv.org/abs/2212.10013

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