Title :
An improved web information summarization based on SSSC
Author :
Tang, Jun ; Zhao, Xiaojuan
Author_Institution :
Dept. of Inf. Eng., Hunan Urban Constr. Coll., Xiangtan, China
Abstract :
This paper proposed a new method of web news summarization via soft clustering algorithm. It used search engine to extract relevant documents, and mixed query sentence into sentences set which segmented from multi-document set, then this paper adopted efficient soft cluster algorithm SSSC to cluster all the sentences. If the number of cluster which contains the query sentence is larger than or equal to 5, the summary sentence will be extracted by turns from the clusters which query sentence in, or feature fusion will be used to extract summary sentences. Experimental result shows that the proposed summarization method can improve the performance of summary, soft clustering algorithm is efficient.
Keywords :
Internet; pattern clustering; query processing; search engines; Web information summarization; Web news summarization; feature fusion; mixed query sentence; relevant document extraction; search engine; sentence similarity-based soft clustering; Asia; Clustering algorithms; Clustering methods; Educational institutions; Frequency estimation; Informatics; Matrix decomposition; Robot control; Robotics and automation; Search engines; Web; sentence similarity; soft clustering; summarization;
Conference_Titel :
Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5192-0
Electronic_ISBN :
1948-3414
DOI :
10.1109/CAR.2010.5456674