Probabilistic Neural Network Based Text Summarization

摘要：

This work proposes an approach to address the problem of improving content selection in automatic text summarization by using probabilistic neural network (PNN). This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features in combination to train the probabilistic neural network (PNN) in order to construct a text summarizer model.

关键词： Automatic Summarization probabilistic neural network statistical model

作者: Mohamed ABDEL FATTAH Fuji REN

作者单位: Faculty of Engineering,University of Tokushima 2-1 Minamijosanjima Tokushima,Japan 770-8506 FIE,Helw Faculty of Engineering,University of Tokushima 2-1 Minamijosanjima Tokushima,Japan 770-8506 School o

会议类型: 国际会议

会议名称: The 2008 IEEE International Conference on Natural Language Processing and Knowledge Engineering(IEEE NLP-KE 2008)(2008IEEE自然语言处理与知识工程国际会议)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-19（万方平台首次上网日期，不代表论文的发表时间）