TY - GEN
T1 - Automating news summarization with sentence vectors offset
AU - Steinert, Mauricio
AU - Granada, Roger
AU - Aires, Joao Paulo
AU - Meneguzzi, Felipe
N1 - 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.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - Automatic text summarization
KW - Information retrieval
KW - Natural language processing
KW - Word embedding
UR - http://www.scopus.com/inward/record.url?scp=85077082628&partnerID=8YFLogxK
U2 - 10.1109/BRACIS.2019.00027
DO - 10.1109/BRACIS.2019.00027
M3 - Published conference contribution
AN - SCOPUS:85077082628
T3 - Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019
SP - 102
EP - 107
BT - Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th Brazilian Conference on Intelligent Systems, BRACIS 2019
Y2 - 15 October 2019 through 18 October 2019
ER -