Text summarization in one of the research area in Natu-ral Language Processing(NLP) which provides meaningfulsummary using various NLP tools and techniques. Sincehuge amount of information is used across the digital worldwhich is very difficult for human beings to manually sum-marize, it is essential to have automatic summarization tech-niques. Summarization techniques are broadly divided intoExtractive and Abstractive. Most automatic summarizationapproaches are extractive which leverage only literal or syn-tactic information in documents. Sentences are extractedfrom the original documents directly by ranking or scoring.On other hand, abstractive summarization is a challengingarea, because it requires deeper analysis of the text and hasthe capability to synthesize a compressed version of the orig-inal sentence or may compose a novel sentence not present inthe original source. The goal of abstractive summarization isto improve the focus of summary, reduce its redundancy andkeeps a good compression rate. This paper is a study of var-ious methods used for abstractive summarization. The mainidea behind these methods has been discussed along with itsstrength and weakness.1IntroductionToday, information is growing very rapidly over theinternet. People use the internet to find information throughinformation retrieval (IR) tools such as Google, Yahoo, Bingand so on. However, with the exponential growth of infor-mation on the internet, information abstraction or summaryof the retrieved results has become necessary for users. Thisbrings text summarization into the picture. Text summariza-tion help users to quickly understand the large volume of in-formation. A document summary keeps its main content,helps user to understand and interpret large volume of text inthe document and reduce user’s time for finding the key in-formation in the document. Summarization done by humanmakes a lot of efforts as, first it is required to read the wholearticle or document, then need to find the key concepts ormain ideas from the article, then finally need to generate anew summary using the main key concepts and ideas. Forhumans, generating a summary is a straight forward processbut it is time consuming. Therefore, the need for automatedsummaries become more and more apparent to automaticallygenerate the summary.Text summarization is the process of extracting salient in-formation from the source text and presenting that informa-tion to the user in the form of summary. It can analyze a mas-sive volume of data and represent it in a concise way. Textsummarization process can be broadly classified into cate-gories, extractive summarization and abstractive summariza-tion. The goal of Extractive summarization is to extract themost significant representative sentences from the text docu-ments and group them to produce a summary. However, ab-stractive summarization requires natural language processingtechniques such as semantic representation, natural languagegeneration, and compression techniques. Abstractive Sum-marization aims to interpret and examines the source text andcreates a concise summary. Extractive summarizers lose alot of information from the input as they only extract a fewimportant sentences from the documents to create the finalsummary. To prevent information loss it is required to aggre-gate information from multiple sentences.Abstractive sum-marization has gained popularity due to its ability of generat-ing new sentences to convey the important information fromtext documents. An abstractive summarizer should presentthe summarized information in a coherent form that is easilyreadable and grammatically correct. Readability or linguisticquality is an important indicator of the quality of a summary.

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