Automatic summarization for chinese text using affinity propagation clustering and latent semantic analysis

Authors:
Rui Yang;Zhan Bu;Zhengyou Xia
Affiliations:
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China;College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China;College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China
Venue:
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
Year:
2012

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Abstract

As the rapid development of the internet, we can collect more and more information. it also means we need the abitily to search the information which really useful to us from the amount of information quickly. Automatic summarization is useful to us for handling the huge amount of text information in the Web. This paper proposes a Chinese summarization method based on Affinity Propagation(AP)clustering and latent semantic analysis(LSA). AP is a new clustering algorithm raised by B. J. Frey on science in 2007 that takes as input measures of similarity between pairs of data points and simultaneously considers all data points as potential exemplars. LSA is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of sentences. Experiment results show that our method could get more comprehensive and high-quality summarization.