Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu


  • Jimmy LinEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_953


Automatic abstracting; Distillation; Report writing; Text/document summarization


Summarization systems generate condensed outputs that convey important information contained in one or more sources for particular users and tasks. In principle, input sources and system outputs are not limited to text (e.g., key frame extraction for video summarization), but this entry focuses exclusively on generating textual summaries from textual sources.

Historical Background

Summarization has a long history dating back to the 1960s, when researchers first started developing computer systems that processed natural language [6, 12]. Following a number of decades with comparatively few publications, summarization research entered a new phase in the 1990s. A revival of interest was spurred by the growing availability of text in electronic formats and later the World Wide Web. The enormous quantities of information people come into contact with on a daily basis created a need for...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Barzilay R, Elhadad M. Using lexical chains for text summarization. In: Proceedings of the ACL/EACL Workshop on Intelligent Scalable Text Summarization; 1997.Google Scholar
  2. 2.
    Barzilay R, Lee L. Catching the drift: probabilistic content models, with applications to generation and summarization. In: Proceedings of the 2004 Human Language Technology Conference; 2004. p. 113–20.Google Scholar
  3. 3.
    Barzilay R, McKeown KR. Sentence fusion for multidocument news summarization. Comput Linguist. 2005;31(3):297–327.zbMATHCrossRefGoogle Scholar
  4. 4.
    Document understanding conferences. http://duc.nist.gov/.
  5. 5.
    Dorr BJ, Monz C, President S, Schwartz R, Zajic D. A methodology for extrinsic evaluation of text summarization: does ROUGE correlate? In: Proceedings of the ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization; 2005.Google Scholar
  6. 6.
    Edmundson HP. New methods in automatic extracting. J ACM. 1969;16(2):264–85.zbMATHCrossRefGoogle Scholar
  7. 7.
    Goldstein J, Mittal V, Carbonell J, Callan J. Creating and evaluating multi-document sentence extract summaries. In: Proceedings of the 9th International Conference on Information and Knowledge Management; 2000. p. 165–72.Google Scholar
  8. 8.
    Hatzivassiloglou V, Klavans JL, Eskin E. Detecting text similarity over short passages: exploring linguistic feature combinations via machine learning. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora; 1999.Google Scholar
  9. 9.
    Knight K, Marcu D. Statistics-based summarization – step one: sentence compression. In: Proceedings of the 17th National Conference on Artificial Intelligence; 2000. p. 703–10.Google Scholar
  10. 10.
    Kupiec J, Pedersen JO, Chen F. A trainable document summarizer. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 1995. p. 68–73.Google Scholar
  11. 11.
    Lin CY, Hovy E. Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 Human Language Technology Conference; 2003. p. 71–8.Google Scholar
  12. 12.
    Luhn HP. The automatic creation of literature abstracts. IBM J Res Dev. 1958;2(2):159–65.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Mani I, Gates B, Bloedorn E. Improving summaries by revising them. In: Proceedings of the 27th Annual Meeting of the Association for Computational Linguistics; 1999. p. 558–65.Google Scholar
  14. 14.
    Marcu D. The rhetorical parsing, summarization, and generation of natural language texts. PhD Thesis, University of Toronto. 1997.Google Scholar
  15. 15.
    Radev DR. Text summarization. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 2004.Google Scholar
  16. 16.
    Radev DR, Blair-Goldensohn S, Zhang Z. Experiments in single and multi-document summarization using MEAD. In: Proceedings of the 2001 Document Understanding Conference; 2001.Google Scholar
  17. 17.
    Radev DR, Hovy E, McKeown K. Introduction to the special issue on summarization. Comput Linguist. 2002;28(4):399–408.CrossRefGoogle Scholar
  18. 18.
    Radev DR, McKeown K. Generating natural language summaries from multiple on-line sources. Comput Linguist. 1998;24(3):469–500.Google Scholar
  19. 19.
    Sparck JK. Automatic summarising: the state of the art. Inf Process Manag. 2007;43(6):1449–81.CrossRefGoogle Scholar
  20. 20.
    Zajic D, Dorr B, Lin J, Schwartz R. Multi-candidate reduction: sentence compression as a tool for document summarization tasks. Inf Process Manage. 2007;43(6):1549–70.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of MarylandCollege ParkUSA

Section editors and affiliations

  • Edie Rasmussen
    • 1
  1. 1.Library, Archival & Inf. StudiesThe Univ. of British ColumbiaVancouverCanada