期刊文献+

Hierarchical Stream Clustering Based NEWS Summarization System 被引量:2

在线阅读 下载PDF
导出
摘要 News feed is one of the potential information providing sources which give updates on various topics of different domains.These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources.In this paper,the news summarization system is proposed for the news data streams from RSS feeds and Google news.Since news stream analysis requires live content,the news data are continuously collected for our experimentation.Themajor contributions of thiswork involve domain corpus based news collection,news content extraction,hierarchical clustering of the news and summarization of news.Many of the existing news summarization systems lack in providing dynamic content with domain wise representation.This is alleviated in our proposed systemby tagging the news feed with domain corpuses and organizing the news streams with the hierarchical structure with topic wise representation.Further,the news streams are summarized for the users with a novel summarization algorithm.The proposed summarization system generates topic wise summaries effectively for the user and no system in the literature has handled the news summarization by collecting the data dynamically and organizing the content hierarchically.The proposed system is compared with existing systems and achieves better results in generating news summaries.The Online news content editors are highly benefitted by this system for instantly getting the news summaries of their domain interest.
出处 《Computers, Materials & Continua》 SCIE EI 2022年第1期1263-1280,共18页 计算机、材料和连续体(英文)
  • 相关文献

参考文献1

二级参考文献25

  • 1Luhn H P. The automatic creation of literature abstracts. IBM Journal of Research and Development, 1958, 2(2): 159- 165.
  • 2Wan X, Yang J, Xiao J. Manifold-ranking based topic-focused multi-document summarization. In Proc IJCAI, Hyderabad, India, Jan. 6-12, 2007, pp.2903-2908.
  • 3Li M, Vitanyi P M. An Introduction to Kolmogorov Complexity and Its Applications. Springer-Verlag, 1997.
  • 4Carbonell J, Goldstein J. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proc. SIGIR, Melbourne, Australia, Aug. 24-28, 1998, pp.335-336.
  • 5Radev D R, Jing H, Stys M, Tam D. Centroid-based summarization of multiple documents. Information Processing and Management, 2004, 40(6): 919-938.
  • 6Kupiec J, Pedersen J, Chen F. A trainable document summarizer. In Proc. SIGIR, Seattle, USA, Jul. 9-13, 1995, pp.68- 73.
  • 7Leskovec J, Milic-Frayling N, Grobelnik M. Impact of linguistic analysis on the semantic graph coverage and learning of document extracts. In Proc. AAAI, Pittsburgh, USA, Jul. 9- 13, 2005, pp.1069-1074.
  • 8Shen D, Sun J T, Li H, Yang Q, Chen Z. Document summarization using conditional random fields. In Proc. IJCAI, Hyderabad, India, Jan. 6-12, 2007, pp.2862-2867.
  • 9Zhang J, Cheng X, Wu G, Xu H. Adasum: An adaptive model for summarization. In Proc. CIKM, Napa Valley, USA, Oct. 26-30, 2008, pp.901-909.
  • 10Erkan G, Radev D R. Lexpagerank: Prestige in multidocument text summarization. In Proc. EMNLP, Barcelona, Spain, Jul. 25-26, 2004, pp.365-371.

共引文献1

同被引文献9

引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部