Tracking and summarizing news on a daily basis with Columbia's Newsblaster

By K. McKeown, Regina Barzilay, D. Evans, Vasileios Hatzivassiloglou, Judith L. Klavans, Ani Nenkova, C. Sable, Barry Schiffman, Sergey Sigelman. 2002

319 citations recorded by the source corpus.

Recently, there have been significant advances in several areas of language technology, including clustering, text categorization, and summarization. However, efforts to combine technology from these areas in a practical system for information access have been limited. In this paper, we present Columbia's Newsblaster system for online news summarization. Many of the tools developed at Columbia over the years are combined together to produce a system that crawls the web for news articles, clusters them on specific topics and produces multidocument summaries for each cluster.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/6902ff693206b75ecf974b8a86713f0f1aa9e87c

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Citations in the corpus

Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment (2011, background): “They proved to be useful in a number of NLP applications such as natural language generation (Iordanskaja et al., 1991), mul-tidocument summarization (McKeown et al., 2002), automatic evaluation of MT (Denkowski and Lavie, 2010), and TE (Dinu and Wang, 2009).”
Introduction to Information Retrieval (2008, unknown): “The Columbia NewsBlaster system (McKeown et al. 2002), a forerunner to the now much more famous and refined Google News (http://news.”
Automatic summarising: The state of the art (2007, background): “Other summarising research has assumed narrower types of audience, for example legal professionals in Moens et al. (1997) and Farzinder and Lapalme (2004), academics in Teufel (2001) and Teufel and Moens (2002).”
DUC in context (2007, methodology): “The NewBlaster system (McKeown et al., 2002) from Columbia University and the WebInEssence system (Radev, Blair-Golddensohn, Zhang, & Raghavan, 2001) from the University of Michigan are several examples of operational systems using multi-document summarization.”
RealTime QA: What's the Answer Right Now? (2022, methodology): “Models developed for the R EAL T IME QA task can be further enhanced with such retrieval/summarization systems.”
Sentence Fusion for Multidocument News Summarization (2005, methodology): “…experiments, including comparisons with human-written summaries in the Document Understanding Conference (DUC) 11 evaluation (McKeown et al. 2001; McKeown et al. 2002) and quality assessment in the context of a particular information access task in the Newsblaster framework (McKeown et al. 2002).”
Paraphrasing with Bilingual Parallel Corpora (2005, unknown): “In multidocument summarization the identification of paraphrases allows information repeated across documents to be condensed (McKeown et al., 2002).”
Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics (2003, background): “For example, Lin and Hovy (2002) pointed out that 18% of the data contained multiple judgments in the DUC 2001 single document evaluation1.”
Personalized news recommendation using classified keywords to capture user preference (2014, methodology): “Document summarization [7] for news is adopted by Yahoo and Google for their news services.”