Yadav, Vrinda. Abstractive text summarization using attention-based stacked LSTM. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2022.
Abstractive text summarization using attention-based stacked LSTM
Abstract:
Every day, the amount of textual data created
increases exponentially, both in terms of complexity and volume.
Massive amounts of information are generated by social media,
news articles, emails, text messages and other resources, making
it difficult to read lengthy language materials. Our main objective
in the paper is to obtain a short understandable and fluent
abstractive summary of any given text. The Abstractive Text
Summarizer automatically gives the summary of the text by
generating new phrase, rephrasing or using the new words which
are not present in the original text. In this paper, a machine
learning architecture i.e. Stacked LSTM based on attention
mechanism using Sequence-to-Sequence model is proposed , to
generate the summary using abstractive approach for Amazon
reviews of fine foods dataset . Our approach allows the model to
accept content and provide a concise summary that may clearly
describe the gist of the original text. The experiments on Amazon
reviews of fine foods dataset show that our model obtained BLEU
Score as 0.91 for a test set
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2024-05-15
Issued:
2024-05-15
บทความ/Article
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BibliograpyCitation :
In IEEE Computer Society. 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT 2022) (pp.236-241). Los Alamitos, CA : IEEE Computer Society