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.
|Title of host publication||Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - Oct 2019|
|Event||8th Brazilian Conference on Intelligent Systems, BRACIS 2019 - Salvador, Bahia, Brazil|
Duration: 15 Oct 2019 → 18 Oct 2019
|Name||Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019|
|Conference||8th Brazilian Conference on Intelligent Systems, BRACIS 2019|
|Period||15/10/19 → 18/10/19|
Bibliographical noteFunding 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.
© 2019 IEEE.
- Automatic text summarization
- Information retrieval
- Natural language processing
- Word embedding