Text Summarization for Light-Weight Web Servers using Unsupervised Methods
In today’s world, Internet has drastically magnified in terms of data. It has lot of documents having information about almost every topic ever been in existence. This boom attracted the researchers towards the field of Text Summarization. The task of pulling out relevant text from word-based contents and restricting its size to not more than 1/3rd of the original document is known as text summarization. This concise text which preserves the information of the original document is known as text summary. When we search something on web, we end up in getting long documents comprising of articles, blogs, etc. It is a waste of time, if after reading the whole text, we find nothing useful. To minimize the time consumed in finding the right article or blog, a text summarizer can be created that can quickly create a concise summary and find whether the text is useful or not. Recently there is a rapid increase in websites that emphasizes on the content generated by the users as there potential contributors. Also, there are many comments and blogs on social media. The proliferation of microblogging and websites like Twitter and Facebook has propelled online communities to prosper by empowering individuals to make, share and disperse free-flowing messages and data. This blast of content has designed an energizing zone in content analysis. Researchers are doing a ton right now in this direction. To automate this undertaking researcher’s face a lot of difficulties as when humans summarize the content, they initially read it and build up their understanding and afterwards they compose the rundown that features the significant key focuses. As computers lacks human’s information and language ability, so it makes their task troublesome. This paper focuses on text summarization technique that obtain a concise summary of text using unsupervised methods. While adopting this technique people can create online text summarizers which can run on affordable web servers using minimal resources. It will be helpful for students to learn about text summarization and develop their own text summarizer web application at minimal cost.