Malagasy Abstractive Text Summarization Using Scheduled Sampling Model

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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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  -