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.
For example, REF extend the standard graph ranking algorithm by proposing a two-layer (sentence layer and topic layer) graphbased semi-supervised learning approach based on topic modeling techniques.
Query-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning
COLING
Computer Science