Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

• DOI: 10.32604/cmc.2022.024556
• Publication Date: 2022-01-18T00:43:38Z
• Authors: Ahmed S. Almasoud; Siwar Ben Haj Has...; Fahd N. Al-Wesabi; Mohamed K. Nour; Anwer Mustafa Hilal; Mesfer Al Duhayyim; Manar Ahmed Hamza; Abdelwahed Motwakel
• Keywords: Multi-document summarization; Data pre-processing; Text processing

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