Automatic Document Summarization using Sentiment Analysis

With the advent of information revolution, electronic documents have become the powerhouse of business and academic information. Modern organizations handle terabytes of data in text format alone. In order to fully understand and utilize these documents, it is necessary to be able to extract the essence of these documents. Having a system that would summarize text would thus be immensely useful in serving this need. For generating a summary, we have to identify the most important and relevant pieces of information from the document, omit irrelevant parts, and assemble them into a compact format. A lot of research has been performed for finding important sentences in a document. This research work focuses on identifying and extracting important parts of the document and to form a coherent summary using sentiment analysis. It uses a degrading extraction approach to create a document summarization framework where in, if one extraction strategy fails then the model gracefully degrades to another.