EM clustering algorithm for automatic text summarization
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
MCMR: Maximum coverage and minimum redundant text summarization model
Expert Systems with Applications: An International Journal
Opposition differential evolution based method for text summarization
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Nowadays, there are commercial tools that allow automatic generation of text summaries. However, it is not known the quality of the generated summaries and the method that it is used for the generation of the summaries using these commercial tools. This paper provides a study about the commercial tools such as Copernic Summarizer, Microsoft Office Word Summarizer 2003 and Microsoft Office Word Summarizer 2007, with the objective to detect which of them gives the summaries more similar to those made by a human. Furthermore, the comparison between commercial tools and state-of-the-art methods is realized. The experiments were carried out using DUC-2002 standard collection which contains 567 news in English.