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
- P Janavarthini, I Antonitte Vinoline, Sustainable fuzzy inventory for concurrent fabrication and material depletion modeling with random substandard items , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Isreal zewide, Abde S. Hajigame, Wondwosen Wondimu, Kibinesh Adimasu, Response of Bread Wheat (Triticum aestivum L.) Varieties to Blended NPSB Fertilizer Levels in Sori Saylem District, South-West Ethiopia , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Mallamma V. Reddy, Sachhidanand Sidramappa, Digitization and Recognition of Kannada Inscription Dynasty , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Shobhit Shukla, Suman Mishra, Gaurav Goel, River flow modeling for flood prediction using machine learning techniques in Godavari river, India , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- N. Saranya, M. Kalpana Devi, A. Mythili, Summia P. H, Data science and machine learning methods for detecting credit card fraud , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- S. Srinithiya, K. Menaka, Optimized Hybrid Feature Selection Techniques for Detecting Iron Deficiency Anemia , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- S. Ranganathan, V. Umadevi, FDBSCAN-MBKSched: A Hybrid Edge-Cloud Clustering and Energy-Aware Federated Learning Framework with Adaptive Update Scheduling for Healthcare IoT , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- Somalee Mahapatra, Manoranjan Dash, Subhashis Mohanty, Adoption of artificial intelligence and the internet of things in dental biomedical waste management , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Subna MP, Kamalraj N, Human Activity Recognition through Skeleton-Based Motion Analysis Using YOLOv8 and Graph Convolutional Networks , The Scientific Temper: Vol. 16 No. 12 (2025): The Scientific Temper
- G. Hemamalini, V. Maniraj, Enhanced otpmization based support vector machine classification approach for the detection of knee arthritis , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 5 6 7 8 9 10 11 12 13 14 > >>
You may also start an advanced similarity search for this article.
