利用维基百科实体增强基于图的多文档摘要

针对基于图的多文档摘要,该文提出了一种在图排序中结合维基百科实体信息增强摘要质量的方法。首先抽取文档集合中高频实体的维基词条内容作为该文档集合的背景知识,然后采用PageRank算法对文档集合中的句子进行排序,之后采用改进的DivRank算法对文档集合和背景知识中的句子一起排序,最后根据两次排序结果的线性组合确定文档句子的最终排序以进行摘要句的选取。在DUC2005数据集上的评测结果表明该方法可以有效利用维基百科知识增强摘要的质量。

Wikipedia Entity to Enhanced Graph-based Multi-document Summarization

This paper presents a novel method to enhance graph-based multi-document summarization by incorporating Wikipedia entities. The Wikipedia contents of high-frequency entities are extracted and arranged as the document collections background knowledge. Then the PageRank algorithm is used to sort these sentences in the document collections and an improved DivRank algorithm is applied to sort the sentences both in the document collections and the background knowledge. Finally the summary sentences are chosen based on a liner combination of these two ranking results. Results of experiments on the data of document understanding conference (DUC) 2005 show that the method proposed in this paper can effectively make use of the Wikipedia knowledge to improve the summary quality.