# A Multitask Cross-Lingual Summary Method Based on ABO Mechanism
## 4 Experiments
### Ablation Study  
This section of the experiments explores the performance improvement of the proposed methods on different scales of cross-linguistic summary datasets, En2 ZhSum and Zh2 EnSum, by controlling the joint models. The experimental models are divided into three parts: Base, which is the base model; Base + RR, which is the model with the addition of reinforced regularity; and Base + RR + ABO, which is the model with the addition of reinforced regularity and the ABO mechanism. Base + RR + ABO uses the original monolingual summary sentence in the dataset. The experimental results are compared in the table below.  
The results in Table 2 indicate that the model achieved a quantitative improvement with the addition of the reinforcement regularity for a small sample size. This is because during training, the same samples are forward computed twice, which is formally equivalent to augmenting the overall dataset, and repeated learning reduces the risk of overfitting the pre-trained model on a small number of samples. Additionally, the sparse output reduces the search range of the predicted words. When combined with the regularization method, it provides higher reliability for a small number of prediction categories under artificial constraints. Thus, the overall reinforced regularity approach improves the effectiveness of the pre-trained model on specific downstream tasks.  
After the addition of the ABO mechanism to the model, the generated summaries showed more improvement in the ROUGE-2 and ROUGE-L scores. This is mainly because the ABO mechanism preserves the proximity of candidate words when selecting anchor  
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Zh2 EnSum**} & \multicolumn{3}{c}{**En2 ZhSum**} \\ \cline{3-8}  & & **RG1** & **RG2** & **RG1** & **RG1** & **RG2** & **RG1** \\ \hline \multirow{3}{*}{Minimum} & Base & 21.61 & 5.88 & 16.19 & 33.04 & 11.45 & 18.04 \\  & Base + RR & 23.53 & 6.45 & 18.25 & 34.14 & 12.38 & 20.17 \\  & Base + RR + ABO & 25.37 & 7.50 & 20.47 & 35.69 & 14.36 & 23.65 \\ \hline \multirow{3}{*}{Medium} & Base & 27.22 & 9.91 & 23.33 & 35.38 & 14.54 & 24.12 \\  & Base + RR & 28.46 & 12.11 & 25.36 & 36.34 & 17.01 & 26.20 \\  & Base + RR + ABO & 30.21 & 14.57 & 27.18 & 37.22 & 18.67 & 28.23 \\ \hline \multirow{3}{*}{Maximum} & Base & 29.35 & 11.78 & 25.23 & 36.35 & 18.44 & 26.14 \\  & Base + RR & 31.10 & 12.93 & 26.51 & 37.24 & 19.87 & 27.26 \\ \cline{1-1}  & Base + RR + ABO & 33.08 & 14.72 & 28.14 & 38.11 & 21.25 & 28.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiments with different sample size data sets.  
words and bound words. As a result, the semantic span between anchor words and bound words is not suddenly increased after filtering by basic grammar rules. Additionally, the proximity of candidate words and phrases of bound words can be used as the basis for the calculation of the same subsequence in 2-g and above. Therefore, the summaries generated by the model incorporating the ABO mechanism usually have higher fluency.