{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:53:08Z","timestamp":1777697588198,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2023,11,20]]},"abstract":"<jats:p>Text summarization techniques offer a way to address the significant challenges faced by clinicians and researchers due to the exponential growth of information in healthcare on the internet. By condensing lengthy text into concise summaries, these techniques facilitate faster, easier, and convenient access to relevant information. This is particularly beneficial in use cases such as online user feedback\/reviews about drugs, where valuable insights can be obtained that extend beyond clinical trials and observational studies. This paper comprehensively evaluates six widely used text summarization techniques (LSA, Luhn\u2019s Method, Text Rank, T5 Transformer, and Kullback-Leibler, BERT) in extracting key insights, themes and patterns about drugs from online drug reviews. The evaluation considers both quantitative and qualitative aspects, focusing on their applicability to the challenging medical terminology, which is known for its inherent intricacies and complexities. The findings of this study showed the performance of text summarization techniques using metrics such as F1 score, Recall, and Precision, focused on the unigram, bigram, and trigram overlap between the generated text summaries and the reference summaries, utilizing the ROUGE-1, ROUGE-2, and ROUGE-L evaluation methods. It is shown that results showed TextRank to be the most effective text summarization method followed by BERT when working with Medical Terminology in Healthcare &amp; Biomedical Informatics, given its complex hierarchy and extensive vocabulary of medical terms.<\/jats:p>","DOI":"10.3233\/idt-230129","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:13:49Z","timestamp":1694776429000},"page":"1309-1322","source":"Crossref","is-referenced-by-count":2,"title":["Evaluation of text summarization techniques in healthcare domain: Pharmaceutical drug feedback"],"prefix":"10.1177","volume":"17","author":[{"given":"Monika","family":"Arora","sequence":"first","affiliation":[{"name":"Department of Information Technology, Bhagwan Parshuram Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pooja","family":"Mudgil","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Bhagwan Parshuram Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Utkarsh","family":"Sharma","sequence":"additional","affiliation":[{"name":"Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaitanya","family":"Chopra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ngangbam Herojit","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/IDT-230129_ref1","doi-asserted-by":"publisher","first-page":"917","DOI":"10.2174\/2213275912666190405114330","article-title":"The inverse edit term frequency for informal word conversion using Soundex for analysis of customer\u2019s reviews","volume":"13","author":"Arora","year":"2020","journal-title":"Recent Advances in Computer Science and Communications"},{"key":"10.3233\/IDT-230129_ref2","doi-asserted-by":"publisher","first-page":"113679","DOI":"10.1016\/j.eswa.2020.113679","article-title":"Automatic text summarization: A comprehensive survey","volume":"165","author":"El-Kassas","year":"2021","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.3233\/IDT-230129_ref3","first-page":"200","article-title":"Using Domain Knowledge for Text Summarization in Medical Domain","volume":"1","author":"Sarkar","year":"2009","journal-title":"International Journal of Recent Trends in Engineering"},{"issue":"703","key":"10.3233\/IDT-230129_ref4","first-page":"10","article-title":"Using Lexical Chains for Text Summarization","volume":"97","author":"Barzilay","year":"1997","journal-title":"Intelligent Scalable Text Summarization"},{"issue":"4","key":"10.3233\/IDT-230129_ref5","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1016\/j.jksuci.2020.05.006","article-title":"Review of Automatic Text Summarization Techniques & Methods","volume":"34","author":"Widyassari","year":"2022","journal-title":"Journal of King Saud University\u00a0\u2013 Computer and Information Sciences"},{"key":"10.3233\/IDT-230129_ref6","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2017.081052"},{"key":"10.3233\/IDT-230129_ref7","unstructured":"NBC Universal News Group. 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