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, Multi-objective Solid Green Trans-shipment Problem for Cold Chain Logistics under Fuzzy Environment , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Regasa Begna, Worku Masho, Wondosan Wondimu, Yaregal Tilahun, Tilahun Bekele, Benyam Tadesse, Haile Negash, Participatory evaluation and demonstration of productive performance of Bovans Brown chicken under village production system in Menit Shasha Woreda, West Omo Zone, Ethiopia , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Aarthi Monalisa M, Anli Suresh, Adoptive bancassurance models transforming patronization among the insured , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- Vinodini R, Ritha W, A green inventory model for deteriorating items while producing overtime with nonlinear cost and stock dependent demand , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper
- N Harini, N Santhi, Challenges and opportunities in product development using natural dyes , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- M. Monika, J. Merline Vinotha, A resilient supply chain model integrating demand variability and carbon emissions in imperfect production systems , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Maysam A. Khabisi, Azar B. Masoudzade, Neda F. Rad, On the effectiveness of receiving teacher and peer feedback as a mediator on Iranian English as a Foreign Language learners’ writing skill: Mobile-mediated vs. direct instruction , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Anli Suresh, Sandhiya M., Investment model on the causation of inclining attributes towards bank investment options in the investor’s portfolio , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- C. Muruganandam, V. Maniraj, A Self-driven dual reinforcement model with meta heuristic framework to conquer the iot based clustering to enhance agriculture production , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Gautam Nayak, Parthivkumar Patel, Developing speaking skills through task-based learning in English as a foreign language classroom , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
