SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling. (arXiv:2005.06377v3 [cs.CL] UPDATED)

Web: http://arxiv.org/abs/2005.06377

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 …

arxiv negative sampling summarization