Tell me more, not just “more of the same” Authors: Francisco Iacobelli, Larry Birnbaum, Kristian J. Hammond Keywords: dimensions of similarity, information retrieval, new information detection
Abstract:
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 how 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.
If you are interested in the automatic extraction, classification and delivery of information, this article is for you.
I think there are (at least) two interesting ways for “Tell Me More” to develop:
First, persisting entity recognition with other data (such as story, author, date, etc.) in the form of associations (with appropriate roles, etc.).
Second, and perhaps more importantly, to enable users to add/correct information presented as part of a mapping of information about particular entities.