TEXT SUMMARIZATION USING HIEARARCHICAL CLUSTERING ALGORITHM AND EXPECTATION MAXIMIZATION CLUSTERING ALGORITHM

Due to an exponential growth in the generation of web data, the need for text summarization of web documents has become very critical. Web data can be accessed by different ways, large amount of data is available which makes searching for relevant pieces of information a difficult task. It is very complicated for human beings to manually summarize large documents of text. Text summarization plays an important role in the area of natural language processing and text mining. Text summarization is compressing the source text into a shorter version preserving its information content and overall meaning. In this paper, we are implementing initially phases of natural language processing that is splitting, tokenization, part of speech tagging, chunking and parsing. Secondly we are implementing Hierarchical clustering Algorithm and Expectation Maximization Clustering Algorithm to find out sentence similarity. Based on the value of sentences similarity, we are summarizing the text document.