Open Access

ARTICLE

Graph Ranked Clustering Based Biomedical Text Summarization Using Top k Similarity

Supriya Gupta^*, Aakanksha Sharaff, Naresh Kumar Nagwani

Department of Computer Science and Engineering, National Institute of Technology, Raipur, 492001, Chhattisgarh, India

* Corresponding Author: Supriya Gupta. Email:

Computer Systems Science and Engineering 2023, 45(3), 2333-2349. https://doi.org/10.32604/csse.2023.030385

Received 25 March 2022; Accepted 01 July 2022; Issue published 21 December 2022

Abstract

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

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