Getting Emotional about News Summarization

  • Alistair Kennedy
  • Anna Kazantseva
  • Diana Inkpen
  • Stan Szpakowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)


News is not simply a straight re-telling of events, but rather an interpretation of those events by a reporter, whose feelings and opinions can often become part of the story itself. Research on automatic summarization of news articles has thus far focused on facts rather than emotions, but perhaps emotions can be significant in news stories too. This article describes research done at the University of Ottawa to create an emotion-aware summarization system, which participated in the Text Analysis Conference last year. We have established that increasing the number of emotional words could help ranking sentences to be selected for the summary, but there was no overall improvement in the final system. Although this experiment did not improve news summarization as evaluated by a variety of standard scoring techniques, it was successful at generating summaries with more emotional words while maintaining the overall quality of the summary.


Average Precision News Article Emotional Word Query Expansion Baseline System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Luhn, H.P.: The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development 2, 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Mithun, S., Kosseim, L.: Summarizing Blog Entries Versus News Text. In: Proceedings of the Workshop on Events in Emerging Text Types. eETTs 2009, Stroudsburg, PA, USA, pp. 1–8. Association for Computational Linguistics (2009)Google Scholar
  3. 3.
    Balahur, A., Lloret, E., Boldrini, E., Montoyo, A., Palomar, M., Martínez-Barco, P.: Summarizing Threads in Blogs Using Opinion Polarity. In: Proceedings of the Workshop on Events in Emerging Text Types, eETTs 2009, Stroudsburg, PA, USA, pp. 23–31. Association for Computational Linguistics (2009)Google Scholar
  4. 4.
    Feng, S., Wang, D., Yu, G., Li, B., Wong, K.-F.: Summarizing and Extracting Online Public Opinion from Blog Search Results. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5981, pp. 476–490. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Nenkova, A., Passonneau, R.J.: Evaluating Content Selection in Summarization: The Pyramid Method. In: Proceedings of HLT-NAACL, Human Language Technology Conference / North American Chapter of the ACL Annual Meeting, pp. 145–152 (2004)Google Scholar
  6. 6.
    Lin, C.Y.: ROUGE: A Package for Automatic Evaluation of summaries. In: Proceedings of the ACL Workshop: Text Summarization Branches Out, pp. 74–81 (2004)Google Scholar
  7. 7.
    Plutchik, R.: A General Psychoevolutionary Theory of Emotion. Emotion: Theory, Research, and Experience 1(3), 3–33 (1980)Google Scholar
  8. 8.
    Mohammad, S.M., Turney, P.D.: Crowdsourcing a Word-Emotion Association Lexicon. To Appear in Computational Intelligence (2012)Google Scholar
  9. 9.
    Givoni, I.E., Frey, B.J.: A Binary Variable Model for Affinity Propagation. Neural Computation 21, 1589–1600 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Kennedy, A., Szpakowicz, S.: Evaluation of a Sentence Ranker for Text Summarization Based on Roget’s Thesaurus. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2010. LNCS, vol. 6231, pp. 101–108. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Copeck, T., Kennedy, A., Scaiano, M., Inkpen, D., Szpakowicz, S.: Summarizing with Roget’s and with FrameNet. In: Second Text Analysis Conference, TAC (2009)Google Scholar
  12. 12.
    Kennedy, A., Copeck, T., Inkpen, D., Szpakowicz, S.: Entropy-Based Sentence Selection with Roget’s Thesaurus. In: Third Text Analysis Conference, TAC (2010)Google Scholar
  13. 13.
    Kennedy, A., Szpakowicz, S.: Evaluating Roget’s Thesauri. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, ACL 2008, pp. 416–424. The Association for Computer Linguistics (2008)Google Scholar
  14. 14.
    Jarmasz, M., Szpakowicz, S.: Roget’s Thesaurus and Semantic Similarity. In: Recent Advances in Natural Language Processing III. Selected Papers from RANLP 2003. CILT, vol. 260, pp. 111–120. John Benjamins, Amsterdam (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alistair Kennedy
    • 1
  • Anna Kazantseva
    • 1
  • Diana Inkpen
    • 1
  • Stan Szpakowicz
    • 1
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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