Advertisement

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)

Abstract

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.

Keywords

Summarization Hierarchical Graph Visible and Invisible Linkage 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allan, J., Gupta, R., Khandelwal, V.: Temporal summaries of new topics. In: Proceedings of the 24th Annual International ACM SIGIR Conference, pp. 10–18 (2001)Google Scholar
  2. 2.
    Erkan, G., Radev, D.R.: Lexpagerank: Prestige in multi-document text summarization. In: Proceedings of EMNLP 2004, pp. 1–7 (2004)Google Scholar
  3. 3.
    Fukumoto, F., Suzuki, Y.: Extracting key paragraph based on topic and event detection: towards multi-document summarization. In: NAACL-ANLP 2000, pp. 31–39 (2000)Google Scholar
  4. 4.
    Kumaran, G., Allan, J.: Text classification and named entities for new event detection. In: Proceedings of the 27th Annual International ACM SIGIR Conference, pp. 297–304 (2004)Google Scholar
  5. 5.
    Li, L., Zhou, K., Xue, G.-R., Zha, H., Yu, Y.: Enhancing diversity, coverage and balance for summarization through structure learning. In: WWW 2009, pp. 71–80 (2009)Google Scholar
  6. 6.
    Lin, C.-Y., Hovy, E.: Automatic evaluation of summaries using N-gram co-occurrence statistics. In: Proceedings of NAACL-HLT 2003, pp. 71–78 (2003)Google Scholar
  7. 7.
    Lin, C.-Y., Hovy, E.: From single to multi-document summarization: a prototype system and its evaluation. In: Proceedings of ACL 2002, pp. 457–464 (2002)Google Scholar
  8. 8.
    Mei, Q., Zhai, C.: Generating Impact-Based Summaries for Scientific Literature. In: Proceedings of ACL 2008, pp. 816–824 (2008)Google Scholar
  9. 9.
    Mihalcea, R., Tarau, P.: A language independent algorithm for single and multiple document summarization. In: Proceedings of IJCNLP 2005, pp. 19–24 (2005)Google Scholar
  10. 10.
    Shen, C., Wang, D., Li, T.: Topic aspect analysis for multi-document summarization. In: Proceedings of CIKM 2010, pp. 1545–1548 (2010)Google Scholar
  11. 11.
    Wan, X., Xiao, J.: Single document keyphrase extraction using neighborhood knowledge. In: Proceedings of AAAI 2008, pp. 855–860 (2008)Google Scholar
  12. 12.
    Wan, X.: An Exploration of document impact on graph-based multi-document summarization. In: Proceedings of EMNLP 2008, pp. 755–762 (2008)Google Scholar
  13. 13.
    Wan, X., Xiao, J.: Graph-based multi-modality learning for topic-focused multi-document summarization. In: Proceedings of IJCAI 2009, pp. 1586–1591 (2009)Google Scholar
  14. 14.
    Wang, D., Zhu, S., Li, T., Gong, Y.: Multi-document summarization using sentence-based topic models. In: Proceedings of ACL/AFNLP 2009 (Short Papers), pp. 297–300 (2009)Google Scholar
  15. 15.
    Wang, D., Li, T.: Document update summarization using incremental hierarchical clustering. In: Proceedings of CIKM 2010, pp. 279–288 (2010)Google Scholar
  16. 16.
    Yan, R., Wan, X., Otterbacher, J., Kong, L., Li, X., Zhang, Y.: Evolutionary timeline summarization: a balanced optimization framework via iterative substitution. In: Proceedings of the 34th Annual International ACM SIGIR Conference, pp. 745–754 (2011)Google Scholar
  17. 17.
    Yan, R., Nie, J.-Y., Li, X.: Summarize what you are interested in: an optimization framework for interactive personalized summarization. In: EMNLP 2011, pp. 1342–1351 (2011)Google Scholar
  18. 18.
    Zhai, C., Lafferty, J.D.: A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. In: Proceedings of SIGIR 2001, pp. 334–342 (2001)Google Scholar

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

Personalised recommendations