Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)

Malagasy Abstractive Text Summarization Using Scheduled Sampling Model

Authors
Volatiana Marielle Ratianantitra1, volatianamarielle@yahoo.fr, Jean Luc Razafindramintsa1, razafindramintsa.jeanluc@yahoo.fr, Thomas Mahatody1, tsmahatody@gmail.com, Claire Rasoamalalavao1, crasoama@gmail.com, Victor Manantsoa1, vmanantsoa@moov.mg
1Laboratory for Mathematical and Computer Applied to the Development Systems, University of Fianarantsoa, Madagascar
Corresponding Author
Volatiana Marielle Ratianantitravolatianamarielle@yahoo.fr
Available Online 2 February 2022.
DOI
10.2991/aisr.k.220201.002How to use a DOI?
Keywords
Abstractive Text Summarization; Deep Learning; Malagasy Language; Neuro-Linguistic Programming
Abstract

Since 1955, text summarizing has evolved. We could observe all the various approaches in several languages, although most of these methods were for significant languages such as English, French, etc. Other scholars have figured out how to summarize their material in their language (a language other than the major languages), which has led us to discover a way for our language, the Malagasy language, which is considered an under-endowed language. An abstractive text summarizing approach is presented in this study. The abstractive technique is more complicated than the extractive approach because it entails re-formulating the source material while maintaining the general idea. However, it results in a more natural summary and better sentence harmony. The Scheduled Sampling approach was utilized to develop the text summarization model, which used deep learning. The task at hand is to teach the model how to communicate in English. The obtained results suggest that deep learning may be applied to the Malagasy language.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
Series
Advances in Intelligent Systems Research
Publication Date
2 February 2022
ISBN
978-94-6239-528-2
ISSN
1951-6851
DOI
10.2991/aisr.k.220201.002How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Volatiana Marielle Ratianantitra
AU  - Jean Luc Razafindramintsa
AU  - Thomas Mahatody
AU  - Claire Rasoamalalavao
AU  - Victor Manantsoa
PY  - 2022
DA  - 2022/02/02
TI  - Malagasy Abstractive Text Summarization Using Scheduled Sampling Model
BT  - Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
PB  - Atlantis Press
SP  - 6
EP  - 9
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.220201.002
DO  - 10.2991/aisr.k.220201.002
ID  - Ratianantitra2022
ER  -