NewSum

Author:

Giannakopoulos George1,Kiomourtzis George2,Karkaletsis Vangelis3

Affiliation:

1. NCSR “Demokritos”, Greece & SciFY Not-for-Profit Company, Greece

2. SciFY Not-for-Profit Company, Greece & NCSR “Demokritos”, Greece

3. NCSR “Demokritos”, Greece

Abstract

This chapter describes a real, multi-document, multilingual news summarization application, named NewSum, the research problems behind it, as well as the novel methods proposed and tested to solve these problems. The system uses the representation of n-gram graphs in a novel manner to perform sentence selection and redundancy removal for the summaries and faces problems related to topic and subtopic detection (via clustering) and multi-lingual applicability, which are caused by the nature of the real-world news summarization sources.

Publisher

IGI Global

Reference72 articles.

1. Exploiting cross-document relations for multi-document evolving summarization.;S. D.Afantenos;Lecture Notes in Artificial Intelligence,2004

2. Allan, J., Wade, C., & Bolivar, A. (2003). Retrieval and novelty detection at the sentence level. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 314–321). ACM.

3. Angheluta, R., De Busser, R., & Moens, M.-F. (2002). The use of topic segmentation for automatic summarization. In Proceedings of the ACL-2002 Workshop on Automatic Summarization. Retrieved from https://www.law.kuleuven.be/icri/publications/51DUC2002.pdf

4. Baralis, E., & Fiori, A. (2010). Summarizing biological literature with BioSumm. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (pp. 1961–1962). Retrieved from http://dl.acm.org/citation.cfm?id=1871785

5. Sentence Fusion for Multidocument News Summarization

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