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RETRACTED: Recent Approaches for Text Summarization Using Machine Learning & LSTM0

Neeraj Kumar Sirohi1,*, Mamta Bansal1, S. N. Rajan2

1 Shobhit Institute of Engineering & Technology, Meerut, 0091121, India
2 IMS Engineering College, Ghaziabad, 0091120, India

* Corresponding Author: Neeraj Kumar Sirohi. Email: email

Journal on Big Data 2021, 3(1), 35-47.


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.


Cite This Article

N. Kumar Sirohi, M. Bansal and S. N. Rajan, "Retracted: recent approaches for text summarization using machine learning & lstm0," Journal on Big Data, vol. 3, no.1, pp. 35–47, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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