Open Access
RETRACTION
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:
Journal on Big Data 2021, 3(1), 35-47. https://doi.org/10.32604/jbd.2021.015954
Received 03 September 2020; Accepted 20 December 2020; Issue published 25 January 2021
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.
Keywords
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. https://doi.org/10.32604/jbd.2021.015954