Advertisement

Interactive System for Automatically Generating Temporal Narratives

  • Arian PasqualiEmail author
  • Vítor Mangaravite
  • Ricardo Campos
  • Alípio Mário Jorge
  • Adam Jatowt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

In this demo, we present a tool that allows to automatically generate temporal summarization of news collections. Conta-me Histórias (Tell me stories) is a friendly user interface that enables users to explore and revisit events in the past. To select relevant stories and temporal periods, we rely on a key-phrase extraction algorithm developed by our research team, and event detection methods made available by the research community. Additionally, we offer the engine as an open source package that can be extended to support different datasets or languages. The work described here stems from our participation at the Arquivo.pt 2018 competition, where we have been awarded the first prize.

Keywords

Information retrieval Temporal summarization 

Notes

Acknowledgements

This work is partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013.

References

  1. 1.
    Aslam, J.A., Ekstrand-Abueg, M., Pavlu, V., Diaz, F., Sakai, T.: TREC 2013 temporal summarization. In: TREC (2013)Google Scholar
  2. 2.
    Campos, R., Mangaravite, V., Pasquali, A., Jorge, A.M., Nunes, C., Jatowt, A.: YAKE! Collection-independent automatic keyword extractor. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 806–810. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-76941-7_80CrossRefGoogle Scholar
  3. 3.
    Campos, R., et al.: A text feature based automatic keyword extraction method for single documents. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 684–691. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-76941-7_63CrossRefGoogle Scholar
  4. 4.
    Corney, D., Albakour, D., Martinez, M., Moussa, S.: What do a million news articles look like? In: Proceedings of the First International Workshop on Recent Trends in News Information Retrieval co-located with 38th European Conference on Information Retrieval (ECIR 2016), Padua, Italy, 20 March 2016, pp. 42–47 (2016)Google Scholar
  5. 5.
    Gomes, D., Cruz, D., Miranda, J., Costa, M., Fontes, S.: Search the past with the Portuguese web archive. In: 22nd International World Wide Web Conference, Rio de Janeiro, Brasil (2013)Google Scholar
  6. 6.
    Jorge, A.M., et al.: Report on the first international workshop on narrative extraction from texts (Text2Story 2018). In: SIGIR Forum, vol. 52, no. 1, pp. 150–152. ACM Press (2018)Google Scholar
  7. 7.
    Schubotz, T., Krestel, R.: Online temporal summarization of news events. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) vol. 1, pp. 409–412 (2015)Google Scholar
  8. 8.
    Tran, G., Alrifai, M., Herder, E.: Timeline summarization from relevant headlines. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 245–256. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16354-3_26CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Arian Pasquali
    • 1
    • 2
    Email author
  • Vítor Mangaravite
    • 1
  • Ricardo Campos
    • 1
    • 3
  • Alípio Mário Jorge
    • 1
    • 2
  • Adam Jatowt
    • 4
  1. 1.LIAAD – INESCTECPortoPortugal
  2. 2.FCUP, University of PortoPortoPortugal
  3. 3.Polytechnic Institute of Tomar - Smart Cities Research CenterTomarPortugal
  4. 4.Kyoto UniversityKyotoJapan

Personalised recommendations