@Article{jbd.2021.015954,
AUTHOR = {Neeraj Kumar Sirohi, Mamta Bansal, S. N. Rajan},
TITLE = {RETRACTED: Recent Approaches for Text Summarization Using Machine  Learning \& LSTM0},
JOURNAL = {Journal on Big Data},
VOLUME = {3},
YEAR = {2021},
NUMBER = {1},
PAGES = {35--47},
URL = {http://www.techscience.com/jbd/v3n1/41299},
ISSN = {2579-0056},
ABSTRACT = {Nowadays, data is very rapidly increasing in every domain such as 
social media, news, education, banking, etc. Most of the data and information is 
in the form of text. Most of the text contains little invaluable information and 
knowledge with lots of unwanted contents. To fetch this valuable information out 
of the huge text document, we need summarizer which is capable to extract data 
automatically and at the same time capable to summarize the document, 
particularly textual text in novel document, without losing its any vital 
information. The summarization could be in the form of extractive and 
abstractive summarization. The extractive summarization includes picking 
sentences of high rank from the text constructed by using sentence and word 
features and then putting them together to produced summary. An abstractive 
summarization is based on understanding the key ideas in the given text and then 
expressing those ideas in pure natural language. The abstractive summarization
is the latest problem area for NLP (natural language processing), ML (Machine 
Learning) and NN (Neural Network) In this paper, the foremost techniques for 
automatic text summarization processes are defined. The different existing 
methods have been reviewed. Their effectiveness and limitations are described. 
Further the novel approach based on Neural Network and LSTM has been 
discussed. In Machine Learning approach the architecture of the underlying 
concept is called Encoder-Decoder.},
DOI = {10.32604/jbd.2021.015954}
}