Less-redundant Text Summarization Using Ensemble Clustering Algorithm Based on GA and PSO

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Abstract: In this paper, a novel text clustering technique is proposed to summarize text documents. The clustering method, so called ‘Ensemble Clustering Method’, combines both genetic algorithms (GA) and particle swarm optimization (PSO) efficiently and automatically to get the best clustering results. The summarization with this clustering method is to effectively avoid the redundancy in the summarized document and to show the good summarizing results, extracting the most significant and non-redundant sentence from clustering sentences of a document. We tested this technique with various text documents in the open benchmark datasets, DUC01 and DUC02. To evaluate the performances, we used F-measure and ROUGE. The experimental results show that the performance capability of our method is about 11% to 24% better than other summarization algorithms.

WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 14, 2017, Art. #4

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Jung Song Lee, Han Hee Hahm, Soon Cheol Park, "Less-redundant Text Summarization Using Ensemble Clustering Algorithm Based on GA and PSO," WSEAS Transactions on Computers, vol. 14, pp. 30-38, 2017, DOI:
Jung Song Lee, Han Hee Hahm, Soon Cheol Park. Less-redundant Text Summarization Using Ensemble Clustering Algorithm Based on GA and PSO. WSEAS Transactions on Computers. 2017;14:30-38.
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