In this paper, we propose a supervised model for ranking word importance that incorporates a rich set of features. Our model is superior to prior approaches for identifying words used in human summaries. Moreover we show that an extractive summarizer which includes our estimation of word importance results in summaries comparable with the state-of-the-art by automatic evaluation.