  Evaluation of summarization tasks is extremely crucial to determining the
quality of machine generated summaries. Over the last decade, ROUGE has become
the standard automatic evaluation measure for evaluating summarization tasks.
While ROUGE has been shown to be effective in capturing n-gram overlap between
system and human composed summaries, there are several limitations with the
existing ROUGE measures in terms of capturing synonymous concepts and coverage
of topics. Thus, often times ROUGE scores do not reflect the true quality of
summaries and prevents multi-faceted evaluation of summaries (i.e. by topics,
by overall content coverage and etc). In this paper, we introduce ROUGE 2.0,
which has several updated measures of ROUGE: ROUGE-N+Synonyms, ROUGE-Topic,
ROUGE-Topic+Synonyms, ROUGE-TopicUniq and ROUGE-TopicUniq+Synonyms; all of
which are improvements over the core ROUGE measures.
