Affiliations: [a] Department of Information Technology, Bhagwan Parshuram Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, India | [b] Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, India | [c] Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India
Correspondence:
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Corresponding author: Utkarsh Sharma, Department of Information Technology, Bhagwan Parshuram Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, India. E-mail: [email protected].
Abstract: 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’s 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 & Biomedical Informatics, given its complex hierarchy and extensive vocabulary of medical terms.
Keywords: Text summarization, pharmaceutical drug feedback, TextRank, biomedical text, NLP in healthcare
