Text Summarization Based on Genetic Programming
作者： Hamid Khosravi , Pooya Khosraviyan Dehkordi , Farshad Kumarci
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摘要: This work proposes an approach to address the problem of improving content selection in automatic text summarization by using some statistical tools. is a trainable summarizer, which takes into account several features, for each sentence generate summaries. First, we investigate effect feature on task. Then use all features combination train genetic programming (GP), vector and fuzzy order construct summarizer model. Furthermore, trained models test performance. The proposed performance measured at compression rates data corpus composed 17 English scientific articles.
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