Graph Ranked Clustering Based Biomedical Text Summarization Using Top k Similarity

Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time effort. Evaluating selecting the most sentences articles is always challenging. This study aims to develop a dual-mode text summarization model achieve enhanced coverage information. The research also includes checking fitment of appropriate graph ranking techniques for improved performance model. input mapped as where meaningful are evaluated central node critical associations between them. proposed framework utilizes top k similarity technique combination UMLS sampled probability-based clustering method which aids unearthing relevant meanings word vectors finding best possible crucial sentences. quality assessed via different parameters like information retention, coverage, readability, cohesion, ROUGE scores non-clustering modes. significant benefits suggested capturing with increased reasonable memory consumption. configurable settings combined reduce execution time, enhance utilization, extract outperforming other baseline models. An improvement 17% achieved when checked against similar summarizers.