A machine translation model for abstractive text summarization based on natural language processing
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.20Keywords:
Machine translation model, Natural language processing, Summarization, Text.Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
“Knowledge is power and knowledge is liberating” conveys that there is a need for the capacity for creativity and that information is plentiful. The key application of natural language processing (NLP) is text summarization. It is a well-known technique for copying text, selecting accurate content, and get insight from the text. The purpose of this study is to propose for providing a summary of the text employing the seq2seq concept from the TensorFlow Python library. Through the use of deep learning-based data augmentation, the suggested method has the potential to increase the effectiveness of the text summary. Finally, the bilingual evaluation understudy (BLEU) criterion is used to judge the effectiveness of the suggested methodologyAbstract
How to Cite
Downloads
Similar Articles
- U. Johns Praveena, J. Merline Vinotha, The multi-objective solid transshipment problem with preservation technology under fuzzy environment , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper
- Vinodini R, Ritha W, The economic order quantity model for sustainable green inventory considers deterioration impact on the real-time replacement and various reorder points with imperfect quality items , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Azar Bagheri Masoudzade, Maryam Ebrahim Nezhad, Appraising social class dimensions on learning motivation of Iranian students: Family studies and their status in focus , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- S ChandraPrabha, S. Kantha Lakshmi, P. Sivaraaj, Data analysis and machine learning-based modeling for real-time production , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- B. Kalpana, P. Krishnamoorthy, S. Kanageswari, Anitha J. Albert, Machine learning approaches for predicting species interactions in dynamic ecosystems , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- A. Basheer Ahamed, M. Mohamed Surputheen, M. Rajakumar, Quantitative transfer learning- based students sports interest prediction using deep spectral multi-perceptron neural network , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- S. C. Prabha, P. Sivaraaj, S. Kantha Lakshmi, Data analysis and machine learning-based modeling for real-time production , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Isreal Zewide, Tamiru Boni, Wondwosen Wondimu, Kibinesh Adimasu, Yield and economics of bean (Phaseolus vulgaris L.) as affected by blended NPS fertilizer rates and inter row spacing at maenitgoldia, Southwest Ethiopia , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Roopesh K R, Jyothi Y, Manisha Bihani, Chandini C H, Nishanth D R, Maheshkumar Hondale, Sairashmi Samanta, Karthik G, Anu M, Neuroprotective effect of alcoholic extract of Selaginella bryopteris leaves in experimental models of epilepsy , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- S. Munawara Banu, M. Mohamed Surputheen, M. Rajakumar, Bio-Inspired and Machine Learning-Driven Multipath Routing Protocol for MANETs Using Predictive Link Analytics , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
<< < 11 12 13 14 15 16 17 18 19 20 > >>
You may also start an advanced similarity search for this article.
