A hybrid model using the pre trained bert and deep neural networks with rich feature for extractive text summarization
A hybrid model using the pre trained bert and deep neural networks with rich feature for extractive text summarization
The pretrained BERT multilingual model is used to generate embedding 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 compared to existing w