Implementasi Aplikasi Peringkasan Teks Otomatis Untuk Atlas Penyakit Menular Pada Manusia Menggunakan Metode Ordered Abstractive Summarization - Caesarardhi, Muhammad Rasyad; Vinarti, Retno Aulia; Kusumawardani, Renny Pradina (Indonesia Association of Computational Linguistics (INACL), 2023)
The death rate for the second pandemic after the first pandemic The Justinian Plague (A.D. 541 to 544) had reached 15-40%. Human depopulation with a loss rate of 50-60% also has been estimated due to the pandemics. Atlas of Human Infectious Disease (AHID) has captured the distribution and determinants for most infectious diseases for humans. AHID is complemented with information about infectious agents, clinical, and epidemiological. Now, every single disease that AHID explains still does not have a visualization with key-point summary on it. It helps people with no medical backgrounds to understand the infectious diseases faster and properly. Interestingly, AHID is semi-structured in terms of data, so we need to automate the process of representing it to a knowledge model. Previous research has shown a rather good improvement in the model of text summarization. Seq2Seq model has reached a ROUGE-1 score of 28,42 and the latest model Bringing in Order to Abstractive Summarization (BRIO) has reached a ROUGE-1 score of 49,07 on the extreme summarization dataset (XSum). Therefore, in this research, we do text summarization for AHID data using the BRIO model and reached score of ROUGE-1 of 43,86. The resulting model therefore can be used to add another disease that is not included yet in AHID such as Covid-19 and output automated text summarization for each one of the attributes. The result of text summarization are delivered as a web-based dictionary.