Automatic Text Document Summarization using Semantic-based Analysis

Since the advent of the web, the amount of data on wen has been increased several million folds. In recent years web data generated is more than data stored for years. One important data format is text. To answer user queries over the internet, and to overcome the problem of information overload one possible solution is text document summarization. This not only reduces query access time, but also optimize the document results according to specific users requirements. Summarization of text document can be categorized as abstractive and extractive. Most of the work has been done in the direction of Extractive summarization. Extractive summarized result is a subset of original documents with the objective of more content coverage and lea redundancy. Our work is based on Extractive approaches. In the first approach, we are using some statistical features and semantic-based features. To include sentiment as a feature is an idea cached from a view that emotion plays an important role. It effectively conveys a message. So, it may play a vital role in text document summarization.Comment: six chapters, 32 figures, 25 tables, 167 pages, phd thesi

A New Lexical Chain Algorithm Used for Automatic Summarization

Abstract. Lexical chains are widely used as a representation of text for Natural Language Processing tasks. However, efficient algorithms for the construction of lexical chains often resort to local decisions. We propose a new algorithm for Lexical Chaining, based on a global function optimization through Relaxation Labelling. A preliminary evaluation of the performance of our approach has been performed on a Catalan agency news corpus. The comparison to an efficient state-ofthe-art algorithm for Lexical Chaining gives promising results. The resulting lexical chainer has been used for a complete multilingual Automatic Summarization system, available on-line