New Methods in Automatic Extracting
Journal of the ACM (JACM)
Journal of Global Optimization
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Document summarization using conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The automatic creation of literature abstracts
IBM Journal of Research and Development
Machine-made index for technical literature: an experiment
IBM Journal of Research and Development
Text summarisation in progress: a literature review
Artificial Intelligence Review
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Extractive single-document summarization based on genetic operators and guided local search
Expert Systems with Applications: An International Journal
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The main challenge of extractive-base text summarization is in selecting the top representative sentences from the input document. Several techniques were proposed to enhance the process of selection such as feature-base, cluster-base, and graph-base methods. Basically, this paper proposed to enhance a previous work, and provides some limitations in the similarity calculation of that previous work. This paper proposes an enhanced mixed feature-base and cluster-base approaches to produce a high qualified single-document summary. We used the Jaccard similarity measure to adjust the sentence clustering process instead of using the Normalized Google Distance (NGD) similarity measure. In addition, this paper proposes a new real-to-integer values modulator instead of using the genetic mutation operator which was adopted in the previous work. The Differential Evolution (DE) algorithm is used for train and test the proposed methods. The DUC2002 dataset was preprocessed and used as a test bed. The results show that our proposed differential mutant presented a satisfied performance while the Genetic mutant proved to be the better. In addition, our analysis of NGD similarity scores showed that NGD was an inappropriate selection in the previous study as it performs successfully in a very big database such as Google. Our selection of Jaccard measure was fortunate and obtained superior results surpassed the NGD using the new proposed modulator and the genetic operator. In addition, both algorithms outperformed the standard baseline Microsoft Word Summarizer and Copernic methods.