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From Unstructured Data to Narrative Abstractive Summaries (Invited Talk)

Author Estela Saquete Boró

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Subject Classification

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

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Abstract

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.

Cite As Get BibTex

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) https://doi.org/10.4230/LIPIcs.TIME.2019.2

Author Details

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

Funding

This research work has been partially funded by the projects PROMETEU/2018/089 and RTI2018-094653-B-C22.

References

  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: https://doi.org/10.1016/j.ipm.2019.02.010.
  2. ISO TimeML Working Group. ISO TimeML TC37 draft international standard DIS 24617-1, 2008. URL: http://semantic-annotation.uvt.nl/ISO-TimeML-08-13-2008-vankiyong.pdf.
  3. Heng Ji, Ralph Grishman, Zheng Chen, and Prashant Gupta. Cross-document Event Extraction and Tracking: Task, Evaluation, Techniques and Challenges. In RANLP, pages 166-172. RANLP 2009 Organising Committee / ACL, 2009. Google Scholar
  4. Hector Llorens, Estela Saquete, and Borja Navarro-Colorado. Applying Semantic Knowledge to the Automatic Processing of Temporal Expressions and Events in Natural Language. Information Processing & Management, 49(1):179-197, 2013. Google Scholar
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