期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：2
出版社：IJCSI Press
摘要：In this paper we propose to produce an extractive summary or given set of documents based on word sequence models by extracting Maximal frequent sequences from the given text. The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. To overcome this problem we have successfully employed the word sequence information from the self-text for detecting the candidate text fragments for composing the summary. To compose the effective summarization we have used mfs technique to extract the detect the most important terms in the source document and Normalized Google dissimilarity distance for sentence clustering. This simple representation not only diminishes domain and language dependency but also enhances the summarization performance.
