A SYSTEMATIC REVIEW ON TEXT SUMMARIZATION OF MEDICAL RESEARCH ARTICLES | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Volume 23, Issue 2, June 2023, Pages 50-61 PDF (391.5 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2023.190004.1252 | ||
| Authors | ||
| Alshimaa M. Ibrahim* 1; Marco Alfonse2; M Aref3 | ||
| 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 | ||
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