Paper: Query-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning

ACL ID C14-1113
Title Query-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

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 algo- rithm 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 perfor- mance.