A HYBRID MODEL USING THE PRETRAINED BERT AND DEEP NEURAL NETWORKS WITH RICH FEATURE FOR EXTRACTIVE TEXT SUMMARIZATION

Authors

  • Tuan Minh Luu National Economics University, Hanoi, Vietnam
  • Huong Thanh Le Hanoi University of Science and Technology
  • Tan Minh Hoang Hanoi University of Science and Technology, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/1813-9663/37/2/15980

Keywords:

Extractive Summarization, BERT multilingual, CNN, Encoder-Decoder, TF-IDF feature

Abstract

Deep neural networks have been applied successfully to extractive text summarization tasks with the accompany of large training datasets. However, when the training dataset is not large enough, these models reveal certain limitations that affect the quality of the system’s summary. In this paper, we propose an extractive summarization system basing on a Convolutional Neural Network and a Fully Connected network for sentence selection. The pretrained BERT multilingual model is used to generate embeddings vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results comparing to existing works using the same dataset. It confirms that our approach can be effectively applied to summarize both English and Vietnamese languages.

Author Biographies

Tuan Minh Luu, National Economics University, Hanoi, Vietnam

National Economics University, Hanoi, Vietnam

Huong Thanh Le, Hanoi University of Science and Technology

School of Information and Telecommunication Technology, Hanoi University of Science and Technology

Tan Minh Hoang, Hanoi University of Science and Technology, Hanoi, Vietnam

School of Information and Telecommunication Technology, Hanoi University of Science and Technology

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Published

2021-05-31

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Section

Computer Science