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Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

Ahmed S. Almasoud1, Siwar Ben Haj Hassine2, Fahd N. Al-Wesabi2,3, Mohamed K. Nour4, Anwer Mustafa Hilal5, Mesfer Al Duhayyim6, Manar Ahmed Hamza5,*, Abdelwahed Motwakel5

1 Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
2 Department of Computer Science, College of Science and Arts at Mahayil, King Khalid University, Saudi Arabia
3 Faculty of Computer and IT, Sana'a University, Sana'a, Yemen
4 Department of Computer Science, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
6 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computers, Materials & Continua 2022, 71(3), 5799-5815.


Due to the advanced developments of the Internet and information technologies, a massive quantity of electronic data in the biomedical sector has been exponentially increased. To handle the huge amount of biomedical data, automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative data. So, multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated information. This paper presents a Deep Learning based Attention Long Short Term Memory (DL-ALSTM) Model for Multi-document Biomedical Text Summarization. The proposed DL-ALSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further processing. Then, the DL-ALSTM model gets executed to summarize the contents from the multiple biomedical documents. In order to tune the summarization performance of the DL-ALSTM model, chaotic glowworm swarm optimization (CGSO) algorithm is employed. Extensive experimentation analysis is performed to ensure the betterment of the DL-ALSTM model and the results are investigated using the PubMed dataset. Comprehensive comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALSTM model with the recently presented models.


Cite This Article

APA Style
Almasoud, A.S., Hassine, S.B.H., Al-Wesabi, F.N., Nour, M.K., Hilal, A.M. et al. (2022). Automated multi-document biomedical text summarization using deep learning model. Computers, Materials & Continua, 71(3), 5799-5815.
Vancouver Style
Almasoud AS, Hassine SBH, Al-Wesabi FN, Nour MK, Hilal AM, Duhayyim MA, et al. Automated multi-document biomedical text summarization using deep learning model. Comput Mater Contin. 2022;71(3):5799-5815
IEEE Style
A.S. Almasoud et al., "Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model," Comput. Mater. Contin., vol. 71, no. 3, pp. 5799-5815. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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