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Abstract
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Article Information:
Semi-Supervised Co-Clustering for Query-Oriented Theme-based Summarization
Libin Yang and Xiaoyan Cai
Corresponding Author: Libin Yang
Submitted: April 16, 2012
Accepted: May 06, 2012
Published: September 15, 2012 |
Abstract:
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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.
Key words: Co-clustering, semi-supervised, theme-based summarization, , , ,
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Abstract
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Cite this Reference:
Libin Yang and Xiaoyan Cai, . Semi-Supervised Co-Clustering for Query-Oriented Theme-based Summarization. Research Journal of Applied Sciences, Engineering and Technology, (18): 3410-3414.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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