An Optimization Text Summarization Method Based on Naïve Bayes and Topic Word for Single Syllable Language

Text summarization since the late 50’s of the 20th century by the simple technical based on term frequency and it applied for technical text summarization at IBM institute. During more than 50 years of development, text summarization is still a hot topic that attracting many researchers, scholars in the field of data mining and natural language processing proposals development of the text summarization system. For the English, there are some automatic text summary systems was built as SUMARIST, SWESUM,... But for single syllable languages like Chinese, Vietnamese, Japanese, Thai, Mongolian and other "native" languages in Southeast and East Asia. Amount people use single syllable language more than 60% of all language on the world. So that, processing of single syllable language is very necessary. However, it is very complex for language processing problem because it’s very hard to determine word or term based on white space and all word segmentation tools not reach 100% accuracy currently. In this paper, we propose a text summarization method based on Naïve Bayes algorithm and topic words set. We’ve experimented with 320 Vietnamese texts (equivalent to 11,670 Vietnamese sentences) show that our method is really effective; text summary is readable, understandable and closer with summary of the human.