In this paper, we propose a generic text summarization method that generates summaries of Turkish texts by ranking sentences according to their scores calculated using their surface level features and extracting the highest ranked ones from the original documents. In order to extract sentences which form a summary with an extensive coverage of main content of the text and less redundancy, we use the features such as term frequency, key phrase, centrality, title similarity and position of the sentence in the original text. Sentence rank is computed using a score function that uses its feature values and the weights of the features. The best feature weights are learned using machine learning techniques with the help of human constructed summaries. Performance evaluation is conducted by comparing summarization outputs with manual summaries generated by 25 independent human evaluators. This paper presents one of the first Turkish summarization systems, and its results are promising.