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2012 (Vol. 4, Issue: 18)
Article Information:

Semi-Supervised Co-Clustering for Query-Oriented Theme-based Summarization

Libin Yang and Xiaoyan Cai
Corresponding Author:  Libin Yang 

Key words:  Co-clustering, semi-supervised, theme-based summarization, , , ,
Vol. 4 , (18): 3410-3414
Submitted Accepted Published
April 16, 2012 May 06, 2012 September 15, 2012

Sentence clustering plays an important role in theme-based summarization which aims to discover the topical themes defined as the clusters of highly related sentences. However, due to the short length of sentences, the word-vector cosine similarity traditionally used for document clustering is no longer suitable. To alleviate this problem, we regard a word as an independent text object rather than a feature of the sentence and develop a noise detection enhanced co-clustering framework to cluster sentences and words simultaneously. We also explore a semi-supervised clustering approach to make the generated summary biased towards the given query. The evaluation conducted on the three DUC query-oriented summarization datasets demonstrates the effectiveness of the approaches.
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  Cite this Reference:
Libin Yang and Xiaoyan Cai, 2012. Semi-Supervised Co-Clustering for Query-Oriented Theme-based Summarization.  Research Journal of Applied Sciences, Engineering and Technology, 4(18): 3410-3414.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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