The Web makes it possible for people to learn more about virtually any news story that interests them. Media outlets try to leverage this information to augment their new stories with links to other "related" stories. However, the approaches to select these stories are either manual and therefore, very expensive to scale or they are automatic and the value of each item of related content is unspecified. In this thesis I will describe Tell Me More, a system that, given a seed news story, identifies paragraphs that contain new information from other news sources that report on the same story. This information is categorized under new names, numbers, quotes and commentary from twitter, according to the nature of its novelty. Tell Me More then presents it alongside the seed news story. Tell Me More strives to select information that is both (a) distinct, because each paragraph selected provides a unique and potentially useful item of information; and (b) categorizable based on the nature of the novelty of each item of information selected, thus making the criteria used to select these items visible to users. I will describe the algorithms used, discuss challenges that Tell Me More faces when it processes news stories and twitter posts, and I will also discuss the contributions of this work. Also, I present evaluations of the system from an algorithmic perspective as well as from a user perspective. Taken together, the design choices and evaluations of Tell Me More provide support to three claims about (a) the feasibility of curating news with distinct information; (b) the feasibility of categorizing this new information; and (c) the fasibility of ranking this information with specialized algorithms that correlate roughly with human ratings.