From Unstructured Data to Narrative Abstractive Summaries (Invited Talk)

Author Estela Saquete Boró

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Estela Saquete Boró
  • Department of Software and Computing Systems, University of Alicante, Apdo. de Correos 99 E-03080, Alicante, Spain

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Estela Saquete Boró. From Unstructured Data to Narrative Abstractive Summaries (Invited Talk). In 26th International Symposium on Temporal Representation and Reasoning (TIME 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 147, pp. 2:1-2:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


To provide with easy and optimal access to digital information, narrative summaries must have a coherent and natural structure. Depending on how a summary is produced, a distinction can be made between extractive and abstractive summaries. Using an abstractive summarization approach, the relevant information (e.g., who? what?, when?, where?,...) could be fused together, leading to the generation of one or more new sentences. However, in order to do this it is necessary to obtain and process the temporal information in a text. A very effective way is the generation of timelines starting from multiple documents so that the generation of summaries is supported by the generated timeline, without losing the relevant temporal information of the texts. In this proposal, a enriched timeline is generated automatically, and the process of generating abstractive summaries is presented using this timeline as a basis [Barros et al., 2019]. Finally, potential applications of the automatic timeline generation would be presented, as for example its application to Fake News detection.

Subject Classification

ACM Subject Classification
  • Applied computing → Document management and text processing
  • Narrative summarization
  • Abstractive summarization
  • Timeline Generation
  • Temporal Information Processing
  • Natural Language Generation


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  1. Cristina Barros, Elena Lloret, Estela Saquete, and Borja Navarro-Colorado. NATSUM: Narrative abstractive summarization through cross-document timeline generation. Information Processing & Management, February 2019. URL:
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