Automating news summarization with sentence vectors offset

Mauricio Steinert, Roger Granada, Joao Paulo Aires, Felipe Meneguzzi

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


Text summaries consist of short versions of texts that convey their key aspects and help readers understand the gist of such texts without reading them in full. Generating such summaries is important for users who must sift through ever-increasing volumes of the content generated on the web. However, generating high-quality summaries is time-consuming for humans and challenging for automated systems, since it involves understanding the semantics of the underlying texts in order to extract key information. In this work, we develop an extractive text summarization method using vector offsets, which we show empirically to be able to summarize texts from an Internet news corpus with an effectiveness competitive with state-of-the-art extractive techniques.

Original languageEnglish
Title of host publicationProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728142531
Publication statusPublished - Oct 2019
Event8th Brazilian Conference on Intelligent Systems, BRACIS 2019 - Salvador, Bahia, Brazil
Duration: 15 Oct 201918 Oct 2019

Publication series

NameProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019


Conference8th Brazilian Conference on Intelligent Systems, BRACIS 2019
CitySalvador, Bahia

Bibliographical note

Funding Information:
Acknowledgements: This study was financed in part by the Coordenac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior (CAPES) and Fundac¸ão de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) agreement (DOCFIX 04/2018). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Publisher Copyright:
© 2019 IEEE.


  • Automatic text summarization
  • Information retrieval
  • Natural language processing
  • Word embedding


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