Volume 11 - Volume 11
Text Summarization Approaches Using Machine Learning & LSTM
Abstract
Due to the massive amount of online textual data generated in a diversity of social media, web, and
other information-centric applications. To select the vital data from the large text, need to study the
full article and generate summary also not loose critical information of text document this process is
called summarization. Text summarization is done either by human which need expertise in that area,
also very tedious and time consuming. second type of summarization is done through system which is
known as automatic text summarization which generate summary automatically. There are mainly two categories of Automatic text summarizations that is abstractive and extractive text summarization.
Extractive summary is produced by picking important and high rank sentences and word from the text
document on the other hand the sentences and word are present in the summary generated through
Abstractive method may not present in original text.
This article mainly focuses on different ATS (Automatic text summarization) techniques that has been
instigated in the present are argue. The paper begin with a concise introduction of automatic text
summarization, then closely discussed the innovative developments in extractive and abstractive text
summarization methods, and then transfers to literature survey, and it finally sum-up with the proposed techniques using LSTM with encoder Decoder for abstractive text summarization are discussed along with some future work directions.
Paper Details
PaperID: 2526
Author's Name: Neeraj Kumar Sirohi, Dr. Mamta Bansal and Dr.S.N. Rajan
Volume: Volume 11
Issues: Volume 11
Keywords: ATA, Text Summarization, Abstractive, Extractive, Neural Network, LSTM, Encoder, Decoder.
Year: 2021
Month: August
Pages: 5010-5026