Abstract: The authors propose an approach of comparative news summarization using co-ranking graph model. The model makes use of the similarity between sentences within each topic and the comparativeness between sentences of different topics, and then calculates the saliences of sentences of both topics simultaneously using an iterative reinforcement approach. Experiment results show the effectiveness of the proposed approach.

Key words: comparative news summarization, comparative text mining, multiple-document summarization, graph ranking model

摘要： 采用协同图排序模型, 为两个可比的新闻话题自动生成对比摘要。利用一个话题内句子之间的相似性, 以及不同话题中句子之间的对比性, 采用迭代增强的方法, 同时计算两个话题中每个句子的重要程度, 并考虑信息的新颖程度, 选择适当的句子组成对比摘要。实验结果表明了该方法的有效性。

关键词: 对比新闻摘要, 对比文本挖掘, 多文档摘要, 图排序