# A Multitask Cross-Lingual Summary Method Based on ABO Mechanism
## 4 Experiments
### Cross-Lingual Summary Comparison Experiments
#### 4.4.3 Human Evaluation  
This section of the manual evaluation aims to compare the usefulness of the generated summaries. We randomly selected 20 samples from each of the En2 ZhSum and Zh2 EnSum test sets, and three evaluators rated the summaries on a scale of 1 (worst) to 5 (best) based on their informativeness (IF), conciseness (CC), and fluency (FL). The average score for each group was calculated based on the total sample size.  
Table 4 shows that the model proposed in this paper outperforms other models in terms of completeness, indirection, and fluency. In terms of fluency, the ABO mechanism combines consecutive fragments, allowing the model to select words that match back and forth, resulting in a subjectively fluent sentence. Regarding conciseness, the model in this paper is similar to the CLS + MS and CLS + MT models, as both set the output with the same sequence length in the encoder part and use alternating tasks in the decoder part. However, our model uses a unified encoder and decoder to integrate multiple tasks, enabling the model to learn the corpus content more comprehensively. Additionally, compared to the multitask framework that uses multiple decoders, our model relies more on the transfer of hidden states between encoders and decoders, resulting in a more holistic content transfer, which provides a clear advantage in terms of completeness and fluency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Zh2 EnSum**} & \multicolumn{3}{c}{**En2 ZhSum**} \\ \cline{2-7}  & **RG1** & **RG2** & **RGL** & **RG1** & **RG2** & **RGL** \\ \hline TLTran & 30.36 & 12.36 & 29.21 & 31.13 & 12.91 & 25.13 \\ TETran & 21.62 & 10.52 & 18.93 & 24.35 & 11.77 & 23.39 \\ TNCLS & 33.21 & 16.24 & 29.04 & 34.40 & 22.34 & 27.05 \\ CLS + MS & 35.35 & 16.67 & 30.28 & 36.82 & 23.75 & 28.71 \\ CLS + MT & 36.09 & 16.71 & 30.16 & 37.13 & 23.22 & 28.75 \\ ours & 37.56 & 17.98 & 32.48 & 38.95 & 24.50 & 30.33 \\ \hline \hline \end{tabular}
\begin{tabular}{c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{Look at the phone for a long time and feel sore eyes?... what exactly does the screen have on vision... News Channel will answer for you today Do you, who use **mobile phones** a lot, feel that your **eyeeight is affected?**} \\ \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Index’s attention**: Will large-screen **mobile phones** cause **Bision loss?**} \\ \hline TLTran & When I look at my phone for a long time, my eyes feel sore. \\ \hline TETran & My eyes feel sore and swollen from looking at my phone for long periods of time. \\ \hline TNCLS & I feel sore eyes when I look at my mobile phone for a long time. \\ \hline \multirow{2}{*}{CLS+MS} & \multirow{2}{*}{Have you felt that your vision is affected by using your **mobile phone** a lot?} \\ \hline \multirow{2}{*}{CLS+MT} & \multirow{2}{*}{Looking at the mobile phone for a long time makes your eyes sore. Do you feel your **vision affected**} \\ \hline \multirow{2}{*}{**ours**} & \multirow{2}{*}{How long does it affect your eyes to look at your **mobile phone? News Channel will answer it for you today.} \\ \end{tabular}
\end{table}
Table 3: Comparison table of cross-lingual summarization experiments.  
Figure 4: Cross-lingual summarization generation example.