  Graph-based semi-supervised learning has proven to be an effective approach
for query-focused multi-document summarization. The problem of previous
semi-supervised learning is that sentences are ranked without considering the
higher level information beyond sentence level. Researches on general
summarization illustrated that the addition of topic level can effectively
improve the summary quality. Inspired by previous researches, we propose a
two-layer (i.e. sentence layer and topic layer) graph-based semi-supervised
learning approach. At the same time, we propose a novel topic model which makes
full use of the dependence between sentences and words. Experimental results on
DUC and TAC data sets demonstrate the effectiveness of our proposed approach.
