1. Combine clustering and frequent itemsets mining to enhance biomedical text summarization
- Author
-
Mustapha Bouakkaz, Hacene Belhadef, and Oussama Rouane
- Subjects
0209 industrial biotechnology ,Apriori algorithm ,Computer science ,business.industry ,General Engineering ,02 engineering and technology ,computer.software_genre ,Automatic summarization ,Computer Science Applications ,Domain (software engineering) ,Term (time) ,Set (abstract data type) ,020901 industrial engineering & automation ,Artificial Intelligence ,Biomedical text ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Cluster analysis ,business ,computer ,Natural language processing - Abstract
Text summarization has become an important research area, especially in the biomedical domain, where information overload is a major problem. In this paper, we propose a novel biomedical text summarization system that combines two popular data mining techniques: clustering and frequent itemset mining. Biomedical paper is expressed as a set of biomedical concepts using the UMLS metathesaurus. The K-means algorithm is used to cluster similar sentences. Then, the Apriori algorithm is applied to discover the frequent itemsets among the clustered sentences. Finally, the salient sentences from each cluster are selected to build the summary using the discovered frequent itemsets. For the evaluation step, we selected randomly 100 biomedical papers from the BioMed Central database full-text, and we evaluated the performances of our system by comparing the resulting summaries with the abstracts of these papers using the ROUGE metrics in term of recall, precision, and F-measure. We also compared the obtained summaries with those achieved by five well-known summarizers: TextRank, TextTeaser, SweSum, ItemSet Based Summarizer, Microsoft AutoSummarize, and two baselines: summarization using only the frequent itemsets mining (FRQ-CL), and summarization using only the clustering (CL-FRQ). The results demonstrate that this combination can successfully enhance the summarization performances, and the proposed system outperforms other tested summarizers.
- Published
- 2019