@inProceedings{
 title = {ParaEval},
 type = {inProceedings},
 year = {2006},
 identifiers = {[object Object]},
 pages = {447-454},
 websites = {http://dl.acm.org/citation.cfm?id=1220892,http://portal.acm.org/citation.cfm?doid=1220835.1220892},
 publisher = {Association for Computational Linguistics},
 city = {Morristown, NJ, USA},
 id = {a3c8bf26-1acb-3e8c-a3b0-a07e1e66459c},
 created = {2013-08-08T06:59:15.000Z},
 accessed = {2013-08-07},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {paraphrasing},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zhoua},
 private_publication = {false},
 abstract = {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.},
 bibtype = {inProceedings},
 author = {Zhou, Liang and Lin, Chin-Yew and Munteanu, Dragos Stefan and Hovy, Eduard},
 booktitle = {Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -}
}

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