An Evaluation for Various Text Summarization Algorithms on Blog Summarization Dataset

This paper aims at finding the accuracy of five different text summarization algorithms when applied on blogs and finding out the most accurate algorithm for creating a highly reliable Automatic summarization tool. The most pertinent aspect of summarization is to find a representative subset of the data, which contains the information of the entire set. Summarization technologies are used in a large number of sectors in industry today. Document summarization tries to automatically create a representative summary or abstract of the entire document, by finding the most informative sentences. Document Summarization of content on the Internet is an enterprise that is widely in demand in current times. Blogs form an integral part of formulating and disseminating popular opinions. A recent estimate revealed that nearly 152 million blogs exist on the internet, creating a dynamic and powerful echo chamber. Therefore, analyzing opinions generated via blogs is integral towards determining trends regarding customer spending, political views, entertainment reviews etc. Information obtained thus can be utilized to carry out further studies across fields like Consumer Spending, Anthropology, Psychology, Politics, Economics etc. Tools for carrying out these summarizations should be in sync with the requirement of the analysis. Different algorithms based on different mathematical and computing concepts are suited for different purposes. Therefore, an analysis of the algorithms itself is imperative towards determining the right approach to take towards analyzing opinions generated via the medium of blogs.