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arXiv · 2005.06377

SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling

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

Canonical automatic summary evaluation metrics, such as ROUGE, focus on lexical similarity which cannot well capture semantics nor linguistic quality and require a reference summary which is costly to obtain. Recently, there have been a growing number of efforts to alleviate either or both of the two drawbacks. In this paper, we present a proof-of-concept study to a weakly supervised summary evaluation approach without the presence of reference summaries. Massive data in existing summarization datasets are transformed for training by pairing documents with corrupted reference summaries. In cross-domain tests, our strategy outperforms baselines with promising improvements, and show a great advantage in gauging linguistic qualities over all metrics.

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Forrest Sheng Bao, Hebi Li, Ge Luo, Minghui Qiu, Yinfei Yang, Youbiao He, Cen Chen. 2020-05-13. SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling. https://arxiv.org/abs/2005.06377

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