Similarity-based Multilingual Multi-Document Summarization

Abstract

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 ROUGE-based evaluations.