Article

Gayathri S, Albertain Institute of Science & Technology; Emmanual Donal ,Albertain Institute of Science & Technology; Roshan Sabu ,Albertain Institute of Science & Technology; Aleena Johnson ,Albertain Institute of Science & Technology; Divya Mohan ,Albertain Institute of Science & Technology

Text Summarization has always been an area of active interest in the academia. In recent times, even though several techniques have being developed for automatic text summarization, efficiency is still a concern. Given the increase in size and number of documents available online, an efficient automatic news summarizer is the need of the hour. In this paper, we propose a technique of text summarization which focuses on the problem of identifying the most important portions of the text and producing coherent summaries. People tend to read multiple news articles on a topic since a single article may not contain all important information. A summary of all the articles related to topic will save the time and energy. In this research, an extractive based approach is used to generate a two-level summary from online news articles. News topics covered include politics, sports health, science and movie reviews from, etc. The first-level summary generates the summary of each article and second level summary combines the first level summaries and generates the final summary. To understand the variation of these news articles, Sentiment Analysis is applied.