Graph-based learning algorithms have been shown to be an effective approach for query-focused multi-document summarization (MDS). In this paper, we extend the standard graph ranking algorithm by proposing a two-layer (i.e. sentence layer and topic layer) graph-based semi-supervised learning approach based on topic modeling techniques. Experimental results on TAC datasets show that by considering topic information, we can effectively improve the summary performance.
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