Enhancing extractive text summarization using natural language processing with an optimal deep learning model

Abdulkhaleq Q. A. Hassan; Badriyya B. Al-onazi; Mashael Maashi; Abdulbasit A. Darem; Ibrahim Abunadi; Ahmed Mahmud; Abdulkhaleq Q. A. Hassan; Badriyya B. Al-onazi; Mashael Maashi; Abdulbasit A. Darem; Ibrahim Abunadi; Ahmed Mahmud

doi:10.3934/math.2024616

AIMS Mathematics

2024, Volume 9, Issue 5: 12588-12609. doi: 10.3934/math.2024616

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Research article Special Issues

Enhancing extractive text summarization using natural language processing with an optimal deep learning model

1.
Department of English, College of Science and Arts at Mahayil, King Khalid University, Saudi Arabia
2.
Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
3.
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi Arabia
4.
Department of Computer Science at the College of Science, Northern Border University, Arar, Saudi Arabi
5.
Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia
6.
Research Center, Future University in Egypt, New Cairo 11835, Egypt

Received: 21 January 2024 Revised: 29 February 2024 Accepted: 06 March 2024 Published: 01 April 2024
MSC : 11Y40

Natural language processing (NLP) performs a vital function in text summarization, a task targeted at refining the crucial information from the massive quantity of textual data. NLP methods allow computers to comprehend and process human language, permitting the development of advanced summarization methods. Text summarization includes the automatic generation of a concise and coherent summary of a specified document or collection of documents. Extracting significant insights from text data is crucial as it provides advanced solutions to end-users and business organizations. Automatic text summarization (ATS) computerizes text summarization by decreasing the initial size of the text without the loss of main data features. Deep learning (DL) approaches exhibited significant performance in abstractive and extractive summarization tasks. This research designed an extractive text summarization using NLP with an optimal DL (ETS-NLPODL) model. The major goal of the ETS-NLPODL technique was to exploit feature selection with a hyperparameter-tuned DL model for summarizing the text. In the ETS-NLPODL technique, an initial step of data preprocessing was involved to convert the input text into a compatible format. Next, a feature extraction process was carried out and the optimal set of features was chosen by the hunger games search optimization (HGSO) algorithm. For text summarization, the ETS-NLPODL model used an attention-based convolutional neural network with a gated recurrent unit (ACNN-GRU) model. Finally, the mountain gazelle optimization (MGO) algorithm was employed for the optimal hyperparameter selection of the ACNN-GRU model. The experimental results of the ETS-NLPODL system were examined under the benchmark dataset. The experimentation outcomes pointed out that the ETS-NLPODL technique gained better performance over other methods concerning diverse performance measures.
- text summarization,
- natural language processing,
- mountain gazelle optimization,
- feature extraction,
- deep learning
Citation: Abdulkhaleq Q. A. Hassan, Badriyya B. Al-onazi, Mashael Maashi, Abdulbasit A. Darem, Ibrahim Abunadi, Ahmed Mahmud. Enhancing extractive text summarization using natural language processing with an optimal deep learning model[J]. AIMS Mathematics, 2024, 9(5): 12588-12609. doi: 10.3934/math.2024616

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Abstract

Natural language processing (NLP) performs a vital function in text summarization, a task targeted at refining the crucial information from the massive quantity of textual data. NLP methods allow computers to comprehend and process human language, permitting the development of advanced summarization methods. Text summarization includes the automatic generation of a concise and coherent summary of a specified document or collection of documents. Extracting significant insights from text data is crucial as it provides advanced solutions to end-users and business organizations. Automatic text summarization (ATS) computerizes text summarization by decreasing the initial size of the text without the loss of main data features. Deep learning (DL) approaches exhibited significant performance in abstractive and extractive summarization tasks. This research designed an extractive text summarization using NLP with an optimal DL (ETS-NLPODL) model. The major goal of the ETS-NLPODL technique was to exploit feature selection with a hyperparameter-tuned DL model for summarizing the text. In the ETS-NLPODL technique, an initial step of data preprocessing was involved to convert the input text into a compatible format. Next, a feature extraction process was carried out and the optimal set of features was chosen by the hunger games search optimization (HGSO) algorithm. For text summarization, the ETS-NLPODL model used an attention-based convolutional neural network with a gated recurrent unit (ACNN-GRU) model. Finally, the mountain gazelle optimization (MGO) algorithm was employed for the optimal hyperparameter selection of the ACNN-GRU model. The experimental results of the ETS-NLPODL system were examined under the benchmark dataset. The experimentation outcomes pointed out that the ETS-NLPODL technique gained better performance over other methods concerning diverse performance measures.

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