  With an ever growing number of extractive summarization techniques being
proposed, there is less clarity then ever about how good each system is
compared to the rest. Several studies highlight the variance in performance of
these systems with change in datasets or even across documents within the same
corpus. An effective way to counter this variance and to make the systems more
robust could be to use inputs from multiple systems when generating a summary.
In the present work, we define a novel way of creating such ensemble by
exploiting similarity between the content of candidate summaries to estimate
their reliability. We define GlobalRank which captures the performance of a
candidate system on an overall corpus and LocalRank which estimates its
performance on a given document cluster. We then use these two scores to assign
a weight to each individual systems, which is then used to generate the new
aggregate ranking. Experiments on DUC2003 and DUC 2004 datasets show a
significant improvement in terms of ROUGE score, over existing sate-of-art
techniques.
