With the flooding of a huge amount of data on the web with reference to the text, a technique to condense this data in summary form is very important so that the users can have access to relevant information regardless of enormous content on the web that is available to the user. This content could be informative, relevant or even important to the user or could be purely irrelevant. Text summarization techniques help in reducing the time and effort of the user looking for content about a particular topic on the internet by summarizing the content of the documents and the user by only looking at the summarized content can decide whether the document is relevant or irrelevant. Thus, automatic text summarization techniques play a key role in information retrieval from the web. In this paper, a study of various text summarization techniques has been conducted based on parameters like a number of documents, content, output, language, availability of training data etc. Also, the summary evaluation processes i.e. intrinsic and extrinsic are discussed. Extractive approaches for text summarization are also discussed and the recent work done in each of these approaches is compared and contrasted.

Text Summarization, Extractive summarization, Intrinsic Evaluation, Extrinsic Evaluation

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