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Similarity-based Multilingual Multi-Document Summarization

We present a new approach for summarizing clusters of documents on the same event, some of which are machine translations of foreign-language documents and some of which are English. Our approach to multilingual multi-document summarization uses text similarity to choose sentences from English documents based on the content of the machine translated documents. A manual evaluation shows that 68% of the sentence replacements improve the summary, and the overall summarization approach outperforms first-sentence extraction baselines in automatic ROUGEbased evaluations.