中文摘要：

时序多文档文摘是针对新闻领域跨时段的相关文档集，即系列新闻报道进行问题无关的、抽取式文摘．根据系列新闻报道不同细节层次的时序特性，提出一种基于宏微观重要性判别模型的内容选择方法．从宏观和微观角度挖掘信息随着时间进化的时序特性，以指导时序多文档文摘的内容选择．首先通过宏观模型确定重要的时间点，然后通过微观模型在重要的时间点选择重要的句子，从而更有效地获取文摘．实验证明该方法是有效的．

英文摘要：

Temporal multi-document summarization （TMDS） aims to capture the evolving information of relevant document sets across periods. Different from the traditional static multi-document summarization, it handles the dynamical collection relevant to a topic. How to resolve the key problems in the temporal context is a new challenge. This paper focuses on how to summarize the series news reports by a generic and extractive way. According to the temporal characteristics of series news reports at different levels of topical detail, a content selection method based on the macro-micro importance discriminative model is proposed. This method mines the temporal characteristics of series news reports from macro and micro views in order to provide the eue for content selection. Firstly, important time points are selected based on the macro importance discriminative model; then important sentences are selected by the micro importance discriminative model; and then these two models are integrated into a macro-micro importance discriminative model. Lastly, summary sentences are ordered chronologically. The experimental results on five groups of Chinese news corpus prove that this method is effective. It also shows that the macro and micro temporal characteristics of series news have the recursive property to some extent and macro coarse filtering helps to the content selection of TMDS.