Automated summarization methods can be defined as "language- independent," if they are not based on any languagespecific knowledge. Such methods can be used for multilingual summarization defined by Mani (2001) as "processing several languages, with summary in the same language as input." In this paper, we introduce MUSE, a languageindependent approach for extractive summarization based on the linear optimization of several sentence ranking measures using a genetic algorithm. We tested our methodology on two languages-English and Hebrew-and evaluated its performance with ROUGE-1 Recall vs. stateof- the-art extractive summarization approaches. Our results show that MUSE performs better than the best known multilingual approach (TextRank1) in both languages. Moreover, our experimental results on a bilingual (English and Hebrew) document collection suggest that MUSE does not need to be retrained on each language and the same model can be used across at least two different languages.