Abstract
In this paper we present NewsInEssence, a fully deployed digital news system. A user selects a current news story of interest which is used as a seed article by NewsInEssence to find in real time other related stories from a large number of news sources. The output is a single document summary presenting the most salient information gleaned from the different sources. We discuss the algorithm used by NewsInEssence, module interoperability, and conclude the paper with a number of empirical analyses.
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Radev, D.R., Blair-Goldensohn, S., Zhang, Z., Raghavan, R.S. (2001). Interactive, Domain-Independent Identification and Summarization of Topically Related News Articles. In: Constantopoulos, P., Sølvberg, I.T. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2001. Lecture Notes in Computer Science, vol 2163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44796-2_20
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DOI: https://doi.org/10.1007/3-540-44796-2_20
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