A SYSTEMATIC REVIEW ON TEXT SUMMARIZATION OF MEDICAL RESEARCH ARTICLES | ||||
International Journal of Intelligent Computing and Information Sciences | ||||
Volume 23, Issue 2, June 2023, Page 50-61 PDF (391.5 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/ijicis.2023.190004.1252 | ||||
View on SCiNiTO | ||||
Authors | ||||
Alshimaa M. Ibrahim 1; Marco Alfonse 2; M Aref 3 | ||||
1Computer Science Department, Faculty of Computer and Information Sciences , Ain Shams University | ||||
2Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt. Laboratoire Interdisciplinaire de l'Université Française d'Égypte (UFEID LAB), Université | ||||
3Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt. | ||||
Abstract | ||||
The term "Medical Text summarization" refers to the process of extracting or collecting more useful information from medical articles in a concise manner. Every day, the count of medical publications increases continuously, and applying text summarization techniques can minimize the time needed to manually transform medical papers into a summarized version. This study's goal is to present a summary of recent works in medical text summarization from 2018 to 2022. It includes 15 papers covering different methodologies such as Clinical Context-Aware (CCA), Prognosis Quality Recognition (PQR), Bidirectional Encoder Representations From Transformers (BERT), Generative Adversarial Networks (GAN), Recurrent Neural Network (RNN), and Sequence-To-Sequence (seq-2-seq) model. Also, the paper describes the newest datasets (PubMed, arXiv, SUMPUBMED, Evidence-Based Medicine Summarization, COVID-19 Open Research, BioMed Central, Clinical Context-Aware, Biomedical Relation Extraction Dataset, Semantic Scholar Open Research Corpus, and Prognosis Quality Recognition) and evaluation metrics (Recall-Oriented Understudy for Gisting Evaluation (ROUGE), F1 Metric, Bilingual Evaluation Understudy (BLEU), BERTScore (BS), and Accuracy) used in medical text summarization. | ||||
Keywords | ||||
Text Summarization; Machine Learning; Natural Language Processing; Medical Papers | ||||
Statistics Article View: 191 PDF Download: 181 |
||||