Sentence Features Fusion for Text Summarization Using Fuzzy Logic

摘要：

The scoring mechanism of the text features is the unique way for determining the key ideas in the text to be presented as text summary. The efficiency of the technique used for scoring the text sentences could produce good summary. The feature scores are imprecise and uncertain, this marks the differentiation between the important features and unimportant is difficult task. In this paper, we introduce fuzzy logic to deal with this problem. Our approach used important features based on fuzzy logic to extract the sentences. In our experiment, we used 30 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we use 9 important features and calculate their score for each sentence. We propose a method using fuzzy logic for sentence extraction and compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the highest average precision, recall, and F-measure for the summaries were obtained from fuzzy method.

关键词： fuzzy logic sentence features text summarization

作者: Ladda Suanmali Mohammed Salem Binwahlan Naomie Salim

作者单位: Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, Thailand 10300 Faculty of Computer Science and Information System, Universiti Teknologi Malaysia 81310

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-5

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）