Entity summarization of news articles
Gianluca Demartini, Malik Muhammad Saad Missen, Roi Blanco, Hugo Zaragoza · 2010
inc.com In this paper we study the problem of entity retrieval for news applications and the importance of the news trail his-tory (i.e. past related articles) to determine the relevant entities in current articles. We construct a novel entity-labeled corpus with temporal information out of the TREC 2004 Novelty collection. We develop and evaluate several features, and show that an article’s history can be exploited to improve its summarization.