Constructing the Multi-Topics and Multi-Document Summarization Algorithm

• Main Authors: CHEN, YU-HSUAN, 陳玉軒
• Other Authors: TU, YI-NING
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/qn8r6c

碩士 === 輔仁大學 === 統計資訊學系應用統計碩士在職專班 === 106 === Document summarization is a very important topic in text mining. In the past, most researches focused on single or multi-document summarization in specific events or topics. However, there has been no summarization research focus on multi-documents in multi-topics in the same time. In this study, the proposed algorithm can cluster the news by analyzing the similarities among multiple news from 736 news stories in 9 different topics, and the clustering accuracy is 0.66. The ROUGE-N score of the proposed algorithm is not only better than TextRank and LexRank summarization but also can find a suitable threshold to process the summary results efficiently.