• Chaitanya Kale
• Swapnil Tawhare
• Dipti Surve

Text summarization is research field which helps to find out detailed but short information from documents which are often large in size, the documents can be from different fields such as finance, news and media, academics, politics, etc. Automatic Text summarization helps people to get more comprehensive information about the document. In other word, the process from which condensed form of document is created which tries to maintain information with out losing or reducing the general meaning of the source document. The main goal is often to maintain the remarkable information. Automatic Text summarization is an important mean by using which large information can be concluded into shorter text in less amount of time and minimal effort. Thus, making it an important field of active research. Approaches of Text summarization are classified into two categories: Extractive and Abstractive. Extractive summarization techniques produce summaries by using words that are present in the document itself. Abstractive model takes a lot of time for training the machine learning model, it involves deep neural network to train the model then and requires large amount of corpus. Collecting such a large amount of corpus (i.e. around 4 to 5 gb minimum) and training time (i.e. about 800 hours minimum) is hard and tedious. This paper focuses on extractive model like Lex Rank and LSA. Using Lex Rank and LSA the big news articles are summarized in order to generate a shorter news article, which is enough for reader to make sense and to get complete idea.

Extractive, Lex Rank, LSA (Latent Semantic Analysis), Stop words, Summarization.

"News Article Summarization using Extractive Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.460-464, March-2019, Available :http://www.jetir.org/papers/JETIR1903563.pdf

"News Article Summarization using Extractive Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp460-464, March-2019, Available at : http://www.jetir.org/papers/JETIR1903563.pdf