Multi-Document Summarization System Based on Mutual Reinforcement Principle

• Main Authors: Yang, Ruin-Min, 楊瑞敏
• Other Authors: Lee, Chia-Hoang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/59722834005586953270

碩士 === 國立交通大學 === 多媒體工程研究所 === 98 === According to the research report, the rapid development of the Internet results in the amount of the digital document, video, or other data to grow in double rate per year. In order to find out the information of these electronic files efficiently, this thesis develops an automatic summarization system to sieve out the non-information data of digital documents. Therefore, users can find out the contents of information efficiently without losing the meaning of the original documents. The automatic summarization system proposed in this thesis considers three different aspects for the sentence scoring: first, the relationship between words and sentences; second, the relationship between the titles and sentences; finally, the relationship between sentences and sentences. Before the sentences scoring, this summarization system uses Alignment algorithm and Mutual Reinforcement Principle to remove the sentences that have fewer information on the original dataset to avoid these sentences with fewer information to be selected as a part of the summary. The HITS algorithm, the cosine similarity calculation methods and the PageRank algorithm are employed respectively to achieve the above three different aspects. The dataset used in this thesis is the DUC dataset, and the constituent documents of the DUC dataset are the English news articles. The evaluation results of the evaluation tools ROUGE show the performance of the summary generate by this summarization system is good.