# An Abstractive Summarization Model Based on Joint-Attention Mechanism and a Priori Knowledge
###### Abstract  
An abstractive summarization model based on the joint-attention mechanism and a priori knowledge is proposed to address the problems of the inadequate semantic understanding of text and summaries that do not conform to human language habits in abstractive summary models. Word vectors that are most relevant to the original text should be selected first. Second, the original text is represented in two dimensions--word-level and sentence-level, as word vectors and sentence vectors, respectively. After this processing, there will be not only a relationship between word-level vectors but also a relationship between sentence-level vectors, and the decoder discriminates between word-level and sentence-level vectors based on their relationship with the hidden state of the decoder. Then, the pointer generation network is improved using a priori knowledge. Finally, reinforcement learning is used to improve the quality of the generated summaries. Experiments on two classical datasets, CNN/DailyMail and DUC 2004, show that the model has good performance and effectively improves the quality of generated summaries. abstractive summarization; joint-attention mechanism; prior knowledge; reinforcement learning +
Footnote †: journal: _Applied Sciences_  
humans and understand more semantic information in the original text, which is even more challenging. The significance of examining it has also been reinforced by the growing rise of generative text summaries in the mainstream of research. Therefore, this article mainly studies generative text summarization. An automatic text summarization model was first designed by [PERSON] [11] in 1958, which kicked off automatic text summarization. Subsequently, graph-based abstractive text summarization [4], linear programming-based abstractive text summarization [5; 6], semantic-based abstractive text summarization [7; 8], and template-based abstractive text summarization [9; 10] followed. With the rise of deep learning in the fields of natural language processing, more and more abstractive text summary models based on deep learning have emerged. Deep-learning-based methods surpass traditional generation methods in multiple languages and evaluation metrics [12]. Deep-learning-based automatic text summarization emulates how people summarize by remembering abstracts based on their comprehension of the original material. As a result, deep-learning-based text summarization technology has received considerable attention from both academia and industry due to its significant research significance and wide range of potential applications. Among many automatic abstract generation models based on deep learning, the most popular one is the sequence-to-sequence (Seq2 seq) [13] model, which is presented in Figure 1. The model generally consists of two parts: the first part is the encoder part, which is used to characterize the input sequence of length n. The second part is the decoder part, which is used to build the representation extracted from the encoder into a mapping of sequence with an output length of m. Most of the existing models are based on this model for improvement. [PERSON] et al. [14] proposed an abstractive text summarization model ABS based on the Seq2 seq model and incorporated the attention mechanism into the model. The Seq2 seq model incorporating attention is shown in Figure 2. Based on the Seq2 seq model, the researchers studied the different effects of different neural networks used for encoder and decoder. [PERSON] et al. [15] constructed decoders based on recurrent neural networks(RNN). [PERSON] et al. [16] changed the encoder to a recurrent neural network as well, making it a standard recurrent neural network-based encoder-decoder model. [PERSON] et al. [17] replaced the recurrent neural network used in the sequence-to-sequence model with a convolutional neural network, thus proposing the ConvS2S model. and the model has achieved good results in automatic text summarization tasks.