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Dragomir R. Radev
Researcher at Yale University
Publications - 336
Citations - 24386
Dragomir R. Radev is an academic researcher from Yale University. The author has contributed to research in topics: Automatic summarization & Computer science. The author has an hindex of 69, co-authored 288 publications receiving 20131 citations. Previous affiliations of Dragomir R. Radev include University of Washington & Columbia University.
Papers
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Journal ArticleDOI
LexRank: graph-based lexical centrality as salience in text summarization
Gunes Erkan,Dragomir R. Radev +1 more
TL;DR: LexRank as discussed by the authors is a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing (NLP), which is based on the concept of eigenvector centrality.
Journal ArticleDOI
Centroid-based summarization of multiple documents
TL;DR: A multi-document summarizer, MEAD, is presented, which generates summaries using cluster centroids produced by a topic detection and tracking system and an evaluation scheme based on sentence utility and subsumption is applied.
TimeML: Robust Specification of Event and Temporal Expressions in Text
James Pustejovsky,José M. Castaño,Robert Ingria,Roser Saurí,Robert Gaizauskas,Andrea Setzer,Graham Katz,Dragomir R. Radev +7 more
TL;DR: TimeML is described, a rich specification language for event and temporal expressions in natural language text, developed in the context of the AQUAINT program on Question Answering Systems, and demonstrated for a delayed (underspecified) interpretation of partially determined temporal expressions.
Proceedings Article
Rumor has it: Identifying Misinformation in Microblogs
TL;DR: This paper addresses the problem of rumor detection in microblogs and explores the effectiveness of 3 categories of features: content- based, network-based, and microblog-specific memes for correctly identifying rumors, and believes that its dataset is the first large-scale dataset on rumor detection.
Journal ArticleDOI
How to Analyze Political Attention with Minimal Assumptions and Costs
TL;DR: This article proposed a topic model for analyzing the substance of political attention, the keywords that identify topics, and the hierarchical nesting of topics in the U.S. Senate from 1997 to 2004.