In this paper we present and evaluate a system for automatic extractive document summarization. We employ three different unsupervised algorithms for sentence ranking - TextRank, K-means clustering and previously unexplored in this field, one- class SVM. By adding language and domain specific boosting we achieve state-of-the-art performance for English measured in ROUGE Ngram(1,1) score on the DUC 2002 dataset - 0,4797. In addition, the system can be used for both single and multi document summarization. We also present results for Swedish, based on a new corpus - featured Wikipedia articles.