ParaEval is an automated evaluation method for comparing reference and peer summaries. It facilitates a tiered comparison strategy where recall-oriented global optimal and local greedy searches for paraphrase matching are enabled in the top tiers. We utilize a domain independent paraphrase table extracted from a large bilingual parallel corpus using methods from Machine Translation (MT). We show that the quality of ParaEval’s evaluations, measured by correlating with human judgments, closely resembles that of ROUGE’s.