Document summarisation based on sentence ranking using vector space model

WWW is a repository of large collection of information available in the form of unstructured documents. It is a challenging task to select the documents of interest from such a huge document pool. To fasten the process of document retrieval, text summarization technique is used. Ranking of documents is made based on the summary or the abstract provided by the authors of the document. But it is not always possible as not all documents come with an abstract or summary. Also when different summarization tools are used to summarize the document, not all the topics covered within the document are reflected in its summary. In this chapter, a method to automate the process of text document summarization is proposed based on the term frequency within the document at different levels – paragraph and sentence. To summarize the document, similarity between the paragraphs and sentences within the paragraph is considered using Vector Space Model. Proposed system evaluation on the standard reference corpus from DUC-2002 using the ROUGE package indicates comparable avg. Recall, avg. Precision and avg. Fmeasure to existing summarization tools – Copernic, SweSum, Extractor, MSWord AutoSummarizer, Intelligent, Brevity, Pertinence taking DUC-2002 (100 words) human summary as baseline summary.