The Web makes it possible for news readers to learn more about virtually any story that interests them. Media outlets and search engines typically augment their information with links to similar stories. It is up to the user to determine what new information is added by them, if any. In this paper we present Tell Me More, a system that performs this task automatically: given a seed news story, it mines the web for similar stories reported by different sources and selects snippets of text from those stories which offer new information beyond the seed story. New content may be classified as supplying: additional quotes, additional actors, additional figures and additional information depending on the criteria used to select it. In this paper we describe how the system identifies new and informative content with respect to a news story. We also show that providing an explicit categorization of new information is more useful than a binary classification (new/not-new). Lastly, we show encouraging results from a preliminary evaluation of the system that validates our approach and encourages further study.

Finding out about a topic online involves visiting multiple news sites, encyclopedia entries, video repositories and other resources while discarding irrelevant information. MakeMy-Page aims to speed the search process by combining automatic aggregation of information with social media to build persistent web pages with images, videos and links to important information about popular topics. Automatic aggregation provides the initial content of the web pages organized by type: blogs, news, web links, images, video and a main article. Social media provides adequate ranking of this content. MakeMyPage creates a main web page about the topic by selecting a few items from each category,and creates secondary webpages with more resources for each of these categories. Users can vote on the resources they like best and, based on these votes, links are promoted to and within the main web page in the appropriate category. In this paper, we argue that this combination of automatic retrieval and social media results in more relevant content about popular topics when compared to both traditional social media aggregators and automatic content aggregators designed to retrieve highly diversified information.