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Combine clustering and frequent itemsets mining to enhance biomedical text summarization
- Source :
- Expert Systems with Applications. 135:362-373
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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.
- 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
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 135
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........139794788dd746078a21aed6473e2690