Clustering-based topic modeling for biomedical documents extractive text summarization

Abstract The increasing volume of electronic text, especially in the biomedical domain, requires automatic text summarization (ATS) to help researchers navigate and find relevant information. This study proposes an unsupervised extractive ATS method to improve the quality of biomedical document summaries by focusing on subtopic diversity. The method integrates topic modeling and clustering with bidirectional encoder representation from transformers (BERT). To evaluate the effectiveness of the proposed study, it tested on a new corpus of 200 biomedical research papers from Biomed Central. The results were evaluated using the ROUGE metric and qualitative evaluation by medical experts. The ROUGE metric yielded scores of 0.4838 (Rouge-1), 0.2174 (Rouge-2), and 0.2206 (Rouge-L), while the qualitative evaluation achieved an average score of 4.10, 4.06, 3.55, 4.0, and 4.0 for completeness, relevance, conciseness, informativity, and readability, respectively. The results demonstrate the effectiveness of the proposed method in summarizing long medical documents.