# An Abstractive Summarization Model Based on Joint-Attention Mechanism and a Priori Knowledge
## 4 Experiment
### Comparison with Public Models  
#### 4.3.1 Results on the Dataset CNN/DailyMail  
On the CNN/DailyMail dataset, we compare the model with the following publicly available models.  
* TextRank [35]: a graph-based ranking algorithm for keyword extraction and document summarization, improved from the PageRank algorithm. It extracts keywords using co-occurrence information (semantics) between words within a document, and can extract keywords, key phrases of a given text from that text, and key sentences of that text using an extractive automatic digest method.
* Seq2 seq + attention [19]: a standard sequence-to-sequence model with an attention mechanism for generating summaries.
* PGN [36]: to solve the problem of words that do not appear in the word list, a pointer generation network is proposed.
* PGN + Cov [36]: an overlay mechanism is added to the pointer generation network to avoid duplication of the generated summaries.
* FASum + FC [37]: a knowledge graph is used to represent the factual information extracted from the article and a factual corrector model is used to correct factual errors.
* ML + RL, intra-attention [18]: introduces a new internal attention mechanism that combines the standard supervised word prediction and reinforcement learning training methods to reduce bias.
* ML + RL Rouge + Novel, with LM [29]: the decoder is divided into a contextual network and a pre-trained language model, and reinforcement learning is used to encourage the generation of new phrases.
* Lightweight Meta-Learning [38]: a low-resource generative summary model using meta-learning and lightweight modules. Effectively mitigates domain transfer and overfitting problems.
* DEATS [39]: proposes a dual-coding model that mutually benefits from the basic model and achieves state-of-the-art results compared to existing methods.
* BERTSumABS [40]: the pre-trained BERTSum is used as the encoder and the 6-layer Transformer [20] is used as the decoder. In addition, a new fine-tuning scheme is proposed to separate the optimizers of the codecs.
* Transformer52S + CS + RL [41]: an FCSF-TABS-based model is proposed, where Bert is fine-tuned for content selection in the first stage and fed into the Transformer-based summary model in the second stage to generate summary sentences.  
Table 1 shows the results of comparing the model in this paper with existing models. It can be seen that JPATS outperforms most publicly available models. Compared with ML + RL, intra-attention and ML + RL Rouge + Novel, with LM, which introduced reinforcement learning, JPATS improved in all three Rouge metrics, which we attribute to the joint-attention mechanism and a priori knowledge, and we will further demonstrate our idea with ablation experiments later. It is worth noting that JPATS is slightly less effective than the models BERTSumABS, Transformer52S + CS + RL, which incorporate Bert and Transformer, reflecting the significant effect of the pre-trained models. We will further focus on the pre-trained model and migrate the methods in this paper to the pre-trained model to obtain better results.  
Table 2 shows the results of the manual evaluation of each model. Our model achieved the highest scores on both readability and general. The presence of a priori knowledge in the JPATS model is shown to help the decoder generate summaries that conform to human reading habits and improve the readability of the summaries. In addition, the model not only requires the decoder to focus on the keywords in the source text, but also forces the  
decoder to focus on the sentences where the keywords are located, and the combination of the two drives the model to generate more comprehensive and specific summaries. Thus, our model is also superior in the two manual evaluation metrics of readability and general. Combining Rouge evaluation metrics and manual evaluation, our model achieves good results.