论文标题
SUENES：通过负抽样评估单案摘要的弱监督方法
SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling
论文作者
论文摘要
规范自动摘要评估指标（例如胭脂）着重于词汇相似性，该词汇相似性无法很好地捕获语义或语言质量，并且需要参考摘要，这是昂贵的。最近，越来越多的努力减轻了这两个缺点中的两种缺点。在本文中，我们将一项概念验证研究提供给弱监督的摘要评估方法，而没有参考摘要。现有摘要数据集中的大量数据通过将文档与损坏的参考摘要配对进行培训。在跨域测试中，我们的策略表现优于基准，具有有希望的改进，并且在计量语言品质上比所有指标都具有很大的优势。
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.
