Hierarchical Graph Summarization: Leveraging Hybrid Information through Visible and Invisible Linkage

  • Rui Yan
  • Zi Yuan
  • Xiaojun Wan
  • Yan Zhang
  • Xiaoming Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)


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.


Summarization Hierarchical Graph Visible and Invisible Linkage 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rui Yan
    • 1
  • Zi Yuan
    • 2
  • Xiaojun Wan
    • 3
  • Yan Zhang
    • 1
  • Xiaoming Li
    • 1
  1. 1.School of Electronics Engineering and Computer SciencePeking UniversityChina
  2. 2.School of Computer Science and EngineeringBeihang UniversityChina
  3. 3.Institute of Computer Science and TechnologyPeking UniversityChina

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