document: Connecting the dots between news articles The process of extracting useful knowledge from large datasets has become one of the most pressing problems in today 's society . The problem spans entire sectors , from scientists to intelligence analysts and web users , all of whom are constantly struggling to keep up with the larger and larger amounts of content published every day . With this much data , it is often easy to miss the big picture . In this paper , we investigate methods for automatically connecting the dots -- providing a structured , easy way to navigate within a new topic and discover hidden connections . We focus on the news domain : given two news articles , our system automatically finds a coherent chain linking them together . For example , it can recover the chain of events starting with the decline of home prices ( January 2007 ) , and ending with the ongoing health-care debate . We formalize the characteristics of a good chain and provide an efficient algorithm ( with theoretical guarantees ) to connect two fixed endpoints . We incorporate user feedback into our framework , allowing the stories to be refined and personalized . Finally , we evaluate our algorithm over real news data . Our user studies demonstrate the algorithm 's effectiveness in helping users understanding the news .
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