Query Dependant Single Document Summarization using Partitional Clustering : K-Means Clustering Approach

A huge amount of on-line information is available on the web, and is still growing. While search engines were developed to deal with this huge volume of documents, even they output a large number of documents for a given user's query. Under these circumstances it became very difficult for the user to find the document he actually needs, because most of the users are reluctant to make the cumbersome effort of going through each of these documents. Therefore systems that can automatically summarize one or more documents are becoming increasingly desirable. A summary can be loosely defined as a text that is produced from one or more texts. Automatic summarization is to use automatic mechanism to produce a finer version for the original document. This paper presents the results of an experimental study of Kmeans document clustering techniques. We have implemented the query dependant single document summarization by using clustering approach. We have implemented k-means algorithm for only . txt file. The performance of the algorithm is analyzed on different evolution factors like execution time, number of words in summary, number of computational loops etc. KeywordsK-means Clustering, Summarization, weighted Document graph,Clustered graph. 1.0 INTRODUCTION Sparck Jones [1] defines summary to be a condensed derivative of source text, i.e. a content reduction through either selection or generalization on what is important in the source. It is a short version of document with only the important information. The definition of the summary, though is an obvious one, emphasizes the fact that summarizing is in general a hard task, because we have to characterize the source text as a whole, we have to capture its important content, where content is a matter of both information and its expression, and importance is a matter of what is essential as well as what is salient. Different kinds of summaries were identified [2, 9, 6, 4] based on the function of the summary. Indicative summaries provide an idea of what the text is about without conveying specific content and informative ones provide some shortened version of the content. Topic-oriented summaries concentrate on the reader's desired topic/topics of interest, whereas generic summaries reflect the author's point of view. Extracts are summaries created by picking portions (words, sentences, etc.) of the input text verbatim, while abstracts are created by regenerating the extracted content. Till now most of the researchers focused on producing extracts, with their concentration being on making the extract either indicative or informative. Indicative summaries usually serve the functions of announcement and screening. By contrast informative summaries are of function of substitution. Critical summaries criticize an approach or an opinion expressed in the text document. Extract can be of announcement and replacement. In general, all of the four types of summaries are retrospective. Indicative and informative summaries are the most important types in the current internet environment. Summaries are influenced by a broad range of factors. The problem is that all the factors and their various manifestations are hard to define, so capturing them precisely enough to guide summarizing in particular cases is very hard. Sparck Jones [9] broadly classified these factors into three types: 1. Input factors: source form, subject type and unit 2. Purpose factors: situation, audience and use 3. Output factors: material, format, style, expression and brevity The summarization is the process of generating Output guided by summary purpose under constraints from input features. Sparck Jones argues automatic text summarization to be a three stage model: I: source text interpretation to source text representation. T: source representation transformation to summary text representation. G: summary text generation from summary representation. Here we discuss general areas of research including singledocument summarization, multi document summarization and query based summarization. 1.1 Single-Document Summarization Despite the research in alternatives to extraction, majority of the work still relies on extraction of sentences from the original document to form the summary. These approaches focused on the development of relatively simple surface-level techniques that tend to signal important passages in the source text. Although most systems use sentences as units, some work has been done with larger passages, typically paragraphs. These techniques for sentence extraction computed a score for each sentence based on features such as position in the text [3,5], word, phrase frequency [10] and key phrases (e.g., \it is important to note") [5]. Recent extraction approaches use more sophisticated techniques for deciding which sentences to extract; these techniques often rely on machine learning to identify important features, on natural language analysis to Giri Virat et al IJCSET | June 2011 | Vol 1, Issue 5,191-193