|Abdelkrime Aries, Djamel Eddine Zegour and Khaled Walid Hidouci|
In this paper, we evaluate our automatic text summarization system in multilingual context. We participated in both single document and multi-document summarization tasks of MultiLing 2015 workshop. Our method involves clustering the document sentences into topics using a fuzzy clustering algorithm. Then each sentence is scored according to how well it covers the various topics. This is done using statistical features such as TF, sentence length, etc. Finally, the summary is constructed from the highest scoring sentences, while avoiding overlap between the summary sentences. This makes it language-independent, but we have to afford preprocessed data first (tokenization, stemming, etc.).