Extensive Study of Automatic Text Summarization on Biomedical Texts
A survey of automatic text summarization techniques and a model which utilizes BERT and Group Average Linkage clustering on articles taken from PubMed is proposed, which would provide a brief summary by condensing the information in a nutshell, thus saving time.
Abstract
Documents containing biomedical information come in a variety of formats. Clinical, research doctors and researchers can use biomedical literature to assess the most recent achievements in a field of study to generate and validate new theories, conduct experiments, and analyze the results. Medical records are difficult to analyze since they are normally presented in plain text, have a very particular technical language, and are almost always unstructured. Due to the ongoing increase of online text resources Document summarization offers a plethora of applications in data extraction and web search. We have done a survey of automatic text summarization techniques. Considering the existing research gaps, we have proposed a model which utilizes BERT and Group Average Linkage clustering on articles taken from PubMed. This work would be helpful for people in the biomedical domain by summarizing huge biomedical texts. It would provide a brief summary by condensing the information in a nutshell, thus saving time.