Improving Multilingual Summarization: Using Redundancy in the Input to Correct MT errors 
 In this paper, we use the information redundancy in multilingual input to correct errors in machine translation and thus improve the quality of multilingual summaries . We consider the case of multi-document summarization , where the input documents are in Arabic , and the output summary is in English . Typically, information that makes it to a summary appears in many different lexical-syntactic forms in the input documents . Further, the use of multiple machine translation systems provides yet more redundancy , yielding different ways to realize that information in English . We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy , focusing on noun phrases . 