Evaluation of text summarization approaches have been mostly based on metrics
that measure similarities of system generated summaries with a set of human
written gold-standard summaries. The most widely used metric in summarization
evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps
between the terms and phrases in the sentences; therefore, in cases of
terminology variations and paraphrasing, ROUGE is not as effective. Scientific
article summarization is one such case that is different from general domain
summarization (e.g. newswire data). We provide an extensive analysis of ROUGE's
effectiveness as an evaluation metric for scientific summarization; we show
that, contrary to the common belief, ROUGE is not much reliable in evaluating
scientific summaries. We furthermore show how different variants of ROUGE
result in very different correlations with the manual Pyramid scores. Finally,
we propose an alternative metric for summarization evaluation which is based on
the content relevance between a system generated summary and the corresponding
human written summaries. We call our metric SERA (Summarization Evaluation by
Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations
with manual scores which shows its effectiveness in evaluation of scientific
article summarization.

本篇论文专门研究了用于科技文章摘要的评估度量，对 ROUGE 的效果进行广泛分析，发现 ROUGE 不太可靠，提出了 SERA 作为科技文章摘要评估的一种有效指标。