Abstract
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.