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Event-Oriented Wiki Document Generation

  • Fangwei Zhu
  • Zhengguo Wang
  • Juanzi LiEmail author
  • Lei Hou
  • Jiaxin Shi
  • Shining Lv
  • Ran Shen
  • Junjun Jiang
Conference paper
  • 8 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12032)

Abstract

We aim to automatically generate event-oriented Wikipedia articles by viewing it as a multi-document summarization problem. In this paper, we propose a new model named WikiGen, which consists of two parts: the first one induces a general topic template from existing Wikipedia articles, and the second one generates a summary for each topic by collecting, filtering, and integrating relevant web news, which will be assembled into the full document. Our evaluation results show that WikiGen is capable of generating fluent and comprehensive Wikipedia documents and outperforms previous work, achieving state-of-the-art ROUGE scores.

Keywords

Wikipedia Text summarization Topic template 

Notes

Acknowledgement

The work is supported by NSFC key projects (U1736204, 61533018, 61661146007), research fund from State Grid Zhejiang Electric Power Research Institute and THUNUS NExT Co-Lab.

References

  1. 1.
    Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study final report. In: Proceedings of the Darpa Broadcast News Transcription & Understanding Workshop (1998)Google Scholar
  2. 2.
    Aula, A.: Query formulation in web information search. In: ICWI (2003)Google Scholar
  3. 3.
    Banerjee, S., Mitra, P.: WikiWrite: generating Wikipedia articles automatically. In: IJCAI (2016)Google Scholar
  4. 4.
    Chu, Y.J., Liu, T.H.: On shortest arborescence of a directed graph. Sci. Sinica 14(10), 1396 (1965)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Edmonds, J.: Optimum branchings. J. Res. Nat. Bureau Standard B 71(4), 233–240 (1967)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hammouda, K.M., Kamel, M.S.: Incremental document clustering using cluster similarity histograms. In: Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI 2003). IEEE (2003)Google Scholar
  7. 7.
    Hu, L., et al.: Learning topic hierarchies for Wikipedia categories. In: ACL (2015)Google Scholar
  8. 8.
    Lebret, R., Grangier, D., Auli, M.: Neural text generation from structured data with application to the biography domain. In: EMNLP (2016)Google Scholar
  9. 9.
    Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: ACL (2004)Google Scholar
  10. 10.
    Liu, P.J., et al.: Generating Wikipedia by summarizing long sequences. arXiv preprint arXiv:1801.10198 (2018)
  11. 11.
    Nallapati, R., Zhai, F., Zhou, B.: SummaRuNNer: a recurrent neural network based sequence model for extractive summarization of documents. In: AAAI (2017)Google Scholar
  12. 12.
    Sauper, C., Barzilay, R.: Automatically generating Wikipedia articles: a structure-aware approach. In: ACL (2009)Google Scholar
  13. 13.
    See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. In: ACL (2017)Google Scholar
  14. 14.
    Shi, J., Liang, C., Hou, L., Li, J., Liu, Z., Zhang, H.: DeepChannel: salience estimation by contrastive learning for extractive document summarization. In: AAAI (2019)Google Scholar
  15. 15.
    Vaswani, A., et al.: Attention is all you need. In: NIPS (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Fangwei Zhu
    • 1
    • 2
    • 3
  • Zhengguo Wang
    • 4
  • Juanzi Li
    • 1
    • 2
    • 3
    Email author
  • Lei Hou
    • 1
    • 2
    • 3
  • Jiaxin Shi
    • 1
    • 2
    • 3
  • Shining Lv
    • 4
  • Ran Shen
    • 4
  • Junjun Jiang
    • 5
  1. 1.DCSTTsinghua UniversityBeijingChina
  2. 2.KIRC, Institute for Artificial IntelligenceTsinghua UniversityBeijingChina
  3. 3.Beijing National Research Center for Information Science and TechnologyBeijingChina
  4. 4.State Grid Zhejiang Electric Power Research InstituteHangzhouChina
  5. 5.State Grid Zhejiang Yuhuan Power Supply CompanyYuhuanChina

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