2009 ExploringContentModelsforMultiD

• (Haghighi & Vanderwende, 2009) ⇒ Aria Haghighi, and Lucy Vanderwende. (2009). “Exploring Content Models for Multi-document Summarization.” In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. ISBN:978-1-932432-41-1

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• http://scholar.google.com/scholar?q=%222009%22+Exploring+Content+Models+for+Multi-document+Summarization
• http://dl.acm.org/citation.cfm?id=1620754.1620807&preflayout=flat#citedby

Quotes

Abstract

We present an exploration of generative probabilistic models for multi-document summarization. Beginning with a simple word frequency based model (Nenkova and Vanderwende, 2005), we construct a sequence of models each injecting more structure into the representation of document set content and exhibiting ROUGE gains along the way. Our final model, HierSum, utilizes a hierarchical LDA-style model (Blei et al., 2004) to represent content specificity as a hierarchy of topic vocabulary distributions. At the task of producing generic DUC-style summaries, HierSum yields state-of-the-art ROUGE performance and in pairwise user evaluation strongly outperforms Toutanova et al. (2007)'s state-of-the-art discriminative system. We also explore HierSum's capacity to produce multiple ' topical summaries' in order to facilitate content discovery and navigation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 ExploringContentModelsforMultiD	Lucy Vanderwende Aria Haghighi			Exploring Content Models for Multi-document Summarization						2009