A Novel Partitioning-Based Clustering Method and Generic Document Summarization
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Automatic generic document summarization based on non-negative matrix factorization
Information Processing and Management: an International Journal
A semantic-based approach to content abstraction and annotation for content management
Expert Systems with Applications: An International Journal
Document summarisation on mobile devices using non-negative matrix factorisation
International Journal of Computer Applications in Technology
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With the proliferation of text data on the World-Wide Web, the development of methods for automatically summarizing these data becomes more important. In this paper, we propose a practical approach for extracting the most relevant sentences from the original document to form a summary. The idea of our approach is to exploit both the local and global properties of sentences. The local property can be considered as clusters of significant words within eachsentence, while the global property can be though of as relations of all sentences in the document. These two properties are combined to get a single measure re.ecting the informativeness of sentences. Experimental results show that our approach compares favorably to a commercial text summarizer.