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With the tremendous amount of news published on the Web every day, helping users explore news events on a given topic of interest is an acute problem. Timeline summaries have recently emerged as a simple and effective solution for users to navigate through temporally related news events. In this paper, we propose an optimization framework and demonstrate the use of Learning To Rank (LTR) to automatically construct timeline summaries from Web news articles. Experimental evaluations show that our approach outperforms existing solutions in producing high quality timeline summaries.