Hanan A. Hosni Mahmoud1,*, Alaaeldin M. Hafez2
CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5121-5136, 2022, DOI:10.32604/cmc.2022.031485
Abstract A substantial amount of textual data is present electronically in several languages. These texts directed the gear to information redundancy. It is essential to remove this redundancy and decrease the reading time of these data. Therefore, we need a computerized text summarization technique to extract relevant information from group of text documents with correlated subjects. This paper proposes a language-independent extractive summarization technique. The proposed technique presents a clustering-based optimization technique. The clustering technique determines the main subjects of the text, while the proposed optimization technique minimizes redundancy, and maximizes significance. Experiments are devised and evaluated using BillSum dataset for… More >
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