Multilingual Single-Document Summarization with MUSE

MUltilingual Sentence Extractor (MUSE) is aimed at multilingual single-document summarization. MUSE implements a supervised language-independent summa-rization approach based on optimization of multiple sentence ranking methods using a Genetic Algorithm. The main advantage of MUSE is its language-independency – it is using statistical sentence features, which can be calculated for sentences in any language. In our previous work, the performance of MUSE was found to be significantly bet-ter than the best known state-of-the-art extractive summarization approaches and tools in three different languages: English, Hebrew, and Arabic. Moreover, our ex-perimental results in the cross-lingual do-main suggest that MUSE does not need to be retrained on a summarization corpus in each new language, and the same weight-ing model can be used across several lan