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Time-Matters: Temporal Unfolding of Texts

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12657)

Abstract

Over the past few years, the amount of information generated, consumed and stored on the Web has grown exponentially, making it impossible for users to keep up to date. Temporal data representation can help in this process by giving documents a sense of organization. Timelines are a natural way to showcase this data, giving users the chance to get familiar with a topic in a shorter amount of time. Despite their importance, little is known about their use in the context of single documents. In this paper, we present Time-Matters, a novel system to automatically explore arbitrary texts through temporal narratives in an interactive fashion that allows users to get insights into the relevant temporal happenings of a story through multiple components, including temporal annotation, storylines or temporal clustering. In contrast to classical timeline multi-document summarization tasks, we focus on performing text summaries of single documents with a temporal lens. This approach may be of interest to a number of providers such as media outlets, for which automatically building a condensed overview of a text is an important issue.

Keywords

  • Timeline generation
  • Temporal narratives
  • Temporal information

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Acknowledgements

Ricardo Campos and Alípio Jorge were financed by the ERDF – European Regional Development Fund through the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145-FEDER-03185). This funding fits under the research line of the Text2Story project. Célia Nunes was financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) through projects UIDB/00212/2020.

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Campos, R. et al. (2021). Time-Matters: Temporal Unfolding of Texts. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_53

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  • DOI: https://doi.org/10.1007/978-3-030-72240-1_53

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