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	Hierarchical graph summarization: Leveraging hybrid information through visible and invisible linkage
	Yan, Rui ; Yuan, Zi ; Wan, Xiaojun ; Zhang, Yan ; Li, Xiaoming
	2012
英文摘要	Graph-based ranking algorithm has been recently exploited for summarization by using sentence-to-sentence relationships. Given a document set with linkage information to summarize, different sentences belong to different documents or clusters (either visible cluster via anchor texts or invisible cluster by semantics), which enables a hierarchical structure. It is challenging and interesting to investigate the impacts and weights of source documents/clusters: sentence from important ones are deemed more salient than the others. This paper aims to integrate three types of hierarchical linkage into traditional graph-based methods by proposing Hierarchical Graph Summarization (HGS). We utilize a hierarchical language model to measure the sentence relationships in HGS. We develop experimental systems to compare 5 rival algorithms on 4 instinctively different datasets which amount to 5197 documents. Performance comparisons between different system-generated summaries and manually created ones by human editors demonstrate the effectiveness of our approach in ROUGE metrics. ? 2012 Springer-Verlag.; EI; 0
语种	英语
DOI标识	10.1007/978-3-642-30220-6_9
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294703]
专题	计算机科学技术研究所 信息科学技术学院
推荐引用方式 GB/T 7714	Yan, Rui,Yuan, Zi,Wan, Xiaojun,et al. Hierarchical graph summarization: Leveraging hybrid information through visible and invisible linkage. 2012-01-01.

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