Leveraging Learning To Rank in an Optimization Framework for Timeline Summarization
Giang Binh Tran, Tuan Tran, Nam-Khanh Tran, Mohammad Alrifai, Nattiya Kanhabua · 2013
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 emerge as a simple and effective solution for users to navigate through tempo-rally 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 arti-cles. Experimental evaluations show that our approach outperforms existing solutions in producing high quality timeline summaries. We make our dataset publicly available for future research in the same area at