  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.
