Multimedia Tools and Applications

, Volume 33, Issue 3, pp 351–377 | Cite as

Story creation from heterogeneous data sources

  • Marat Fayzullin
  • V. S. Subrahmanian
  • Massimiliano Albanese
  • Carmine Cesarano
  • Antonio Picariello
Article

Abstract

There are numerous applications where there is a need to rapidly infer a story about a given subject from a given set of potentially heterogeneous data sources. In this paper, we formally define a story to be a set of facts about a given subject that satisfies a “story length” constraint. An optimal story is a story that maximizes the value of an objective function measuring the goodness of a story. We present algorithms to extract stories from text and other data sources. We also develop an algorithm to compute an optimal story, as well as three heuristic algorithms to rapidly compute a suboptimal story. We run experiments to show that constructing stories can be efficiently performed and that the stories constructed by these heuristic algorithms are high quality stories. We have built a prototype STORY system based on our model—we briefly describe the prototype as well as one application in this paper.

Keywords

Multimedia Heterogenous Databases Summarization Stories Storytelling Framework Algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal R, Bayardo R, Srikant R (2000) Athena: mining-based interactive management of text databases. In: Proc. intl. conf. on extending database technology. Lecture notes in computer science, vol. 1777. Springer, Berlin Heidelberg New York, pp 365–379Google Scholar
  2. 2.
    Bers M, Ackermann E, Cassell J, Donegan B, Gonzalez-Heydrich J, DeMaso D, Strohecker C, Lualdi S, Bromley D, Karlin J (1998) Interactive storytelling environments: coping with cardiac illness at Boston’s Children’s Hospital. In: Proc. CHI-1998. ACM, New York, pp 603–610Google Scholar
  3. 3.
    Callan J, Mitamura T (2002) Knowledge-based extraction of named entities. In: Proc. 4th int. conf. on information and knowledge management. ACM, New York, pp 532–537Google Scholar
  4. 4.
    de Oliverira IL, Wazlawick RS, (1998) A modular connectionist parser for resolution of pronominal anaphoric references in multiple sentences. In: Proc. int. joint conf. on neural networks. IEEE world congress on computational intelligence, vol 2, pp 1194–1199, MayGoogle Scholar
  5. 5.
    Fayzullin M, Subrahmanian VS, Picariello A, Sapino ML (2005) The CPR model for summarizing video. Multimedia Tools and Applications 26(2):153–173CrossRefGoogle Scholar
  6. 6.
    Francis WN (1979) Brown corpus manual. http://helmer.aksis.uib.no/icame/brown/bcm.html
  7. 7.
    GuoDong Z, Jian S (2003) Integrating various features in hidden Markov model using constraint relaxation algorithm for recognition of named entities without gazetteers. In: Proc. int. conf. on natural language processing and knowledge engineering, pp 465–470, OctoberGoogle Scholar
  8. 8.
    Jamil HM, Lakshmanan LVS (1995) A declarative semantics for behavioral inheritance and conflict resolution. In: Lloyd J (ed) Proc. of the 12th international logic programming symposium (ILPS). MIT Press, Portland, Oregon, pp 130–144, 4–7 December 1995Google Scholar
  9. 9.
    Langdon WB, Poli R (2002) Foundations of genetic algorithms. Springer, Berlin Heidelberg New YorkGoogle Scholar
  10. 10.
    Machado I, Prada R, Paiva A (2000) Bringing drama into a virtual stage. Proc. CVE-2000Google Scholar
  11. 11.
    Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38(11):39–41, NovemberCrossRefGoogle Scholar
  12. 12.
    Neuhoff D (1975) The Viterbi algorithm as an aid in text recognition. IEEE Trans Inf Theory 21(2):222–226CrossRefGoogle Scholar
  13. 13.
    Oyama S, Kokubo T, Ishida T (2004) Domain-specific web search with keyword spices. IEEE Trans Knowl Data Eng 13(1):17–27, JanuaryCrossRefGoogle Scholar
  14. 14.
    Rosso P, Masulli F, Buscaldi D (2003) Word sense disambiguation combining conceptual distance, frequency and gloss. In: Proc. int. conf. on natural language processing and knowledge engineering, pp 120–125, OctoberGoogle Scholar
  15. 15.
    Theune M, Faas S, Nijholt A, Heylen D (2003) The virtual storyteller: story creation by intelligent agents. In: Proc. of technologies for interactive digital storytelling and entertainment conference, pp 204–215Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Marat Fayzullin
    • 1
  • V. S. Subrahmanian
    • 1
  • Massimiliano Albanese
    • 2
  • Carmine Cesarano
    • 2
  • Antonio Picariello
    • 2
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  2. 2.Dipartimento di Informatica e SistemisticaUniversità di Napoli “Federico II”NapoliItaly

Personalised recommendations