TELKOMNIKA Telecommunication, Computing, Electronics and Control
Summarization of COVID-19 news documents deep learning-based using transformer architecture
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Summarization of COVID-19 news documents deep learning-based using transformer architecture
Summarization of COVID-19 news documents deep learning-based using transformer architecture
Subject
COVID-19
Deep learning
News summarization
Transformer architecture
Deep learning
News summarization
Transformer architecture
Description
Facing the news on the internet about the spreading of Corona virus disease
2019 (COVID-19) is challenging because it is required a long time to get
valuable information from the news. Deep learning has a significant impact on
NLP research. However, the deep learning models used in several studies,
especially in document summary, still have a deficiency. For example, the
maximum output of long text provides incorrectly. The other results are
redundant, or the characters repeatedly appeared so that the resulting sentences
were less organized, and the recall value obtained was low. This study aims to
summarize using a deep learning model implemented to COVID-19 news
documents. We proposed transformer as base language models with
architectural modification as the basis for designing the model to improve
results significantly in document summarization. We make a
transformer-based architecture model with encoder and decoder that can be
done several times repeatedly and make a comparison of layer modifications
based on scoring. From the resulting experiment used, ROUGE-1 and
ROUGE-2 show the good performance for the proposed model with scores
0.58 and 0.42, respectively, with a training time of 11438 seconds. The model
proposed was evidently effective in improving result performance in
abstractive document summarization.
2019 (COVID-19) is challenging because it is required a long time to get
valuable information from the news. Deep learning has a significant impact on
NLP research. However, the deep learning models used in several studies,
especially in document summary, still have a deficiency. For example, the
maximum output of long text provides incorrectly. The other results are
redundant, or the characters repeatedly appeared so that the resulting sentences
were less organized, and the recall value obtained was low. This study aims to
summarize using a deep learning model implemented to COVID-19 news
documents. We proposed transformer as base language models with
architectural modification as the basis for designing the model to improve
results significantly in document summarization. We make a
transformer-based architecture model with encoder and decoder that can be
done several times repeatedly and make a comparison of layer modifications
based on scoring. From the resulting experiment used, ROUGE-1 and
ROUGE-2 show the good performance for the proposed model with scores
0.58 and 0.42, respectively, with a training time of 11438 seconds. The model
proposed was evidently effective in improving result performance in
abstractive document summarization.
Creator
Nur Hayatin, Kharisma Muzaki Ghufron, Galih Wasis Wicaksono
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Oct 23, 2020
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Nur Hayatin, Kharisma Muzaki Ghufron, Galih Wasis Wicaksono, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Summarization of COVID-19 news documents deep learning-based using transformer architecture,” Repository Horizon University Indonesia, accessed April 12, 2025, https://repository.horizon.ac.id/items/show/3839.
Summarization of COVID-19 news documents deep learning-based using transformer architecture,” Repository Horizon University Indonesia, accessed April 12, 2025, https://repository.horizon.ac.id/items/show/3839.