Frequent item-set mining and clustering based ranked biomedical text summarization

The difficulty of deriving value out of vast available scientific literature in a condensed form lead us to look for a proficient theme based summarization solution which can preserve precise biomedical content. The study targets to analyze impact of combining semantic biomedical concepts extraction, frequent item-set mining and clustering techniques over information retention, objective functions and ROUGE values for the obtained final summary. The suggested frequent item-set mining and clustering (FRI-CL) graph-based framework uses UMLS metathesarus and BERT-based semantic embeddings to identify domain-relevant concepts. The scrutinized concepts are mined according to their relationship with neighbors and frequency via an amended FP-Growth model. The framework utilizes S-DPMM clustering, which is a probabilistic mixture model and aids in the identification and clubbing of complex relevant patterns to increase coverage of important sub-themes. The sentences with the frequent concepts are scored via PageRank to form an efficient and compelling summary. The research experiments on the 100 sample biomedical documents taken from PubMed archives are evaluated via calculation of ROUGE scores, coverage, readability, non-redundancy, memory utilization and information retention from the summary output. The results with the FRI-CL summarization system showcased 10% ROUGE performance improvement and are at par with the other baseline methods. On an average 30–40% improvement in memory utilization is observed with up to 50% information retention when experiments are performed using S-DPMM clustering. The research indicates that the fusion of semantic mapping, clustering, along with frequent-item set mining of biomedical concepts enhance the overall co-related information covering all sub-themes.