## 4 Experiments
### Results
The results presented in Tables 2 and 3 demonstrate that our method achieved comparable performance to that of early supervised large models and even outperformed them in several metrics, despite using only a lightweight model and a limited amount of data. However, the performance of recent supervised models was still better than that of our semi-supervised method. For instance, on CSL, our best results achieved over 93% of the fully supervised BERT-base's performance on every metric, significantly outperforming LSTM-seq2 seq and ALBERT-tiny. Regarding LCSTS, our model achieved better results than the best early fully supervised model, RNN-context-Char, by about 6%, and it had a score that was approximately 81% of the ROUGE-L of recent models, such as mT5 and CPM2. The experimental results confirm the effectiveness of our proposed approach in automatic text summarization. In addition to comparing our results with those of other models, it is important to highlight the comparison between the results of our models and that of the original T5 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **BLEU** \\ \hline ALBERT-tiny & 52.75 & 37.96 & 48.11 & 21.63 \\ BERT-base & 63.83 & 51.29 & 59.76 & 41.45 \\ BERT-wwm-ext & 63.44 & 51 & 59.4 & 41.19 \\ RoBERTa-wwm-ext & 63.23 & 50.74 & 58.99 & 41.31 \\ LSTM-seq2 seq & 46.48 & 30.48 & 41.8 & 22 \\ \hline Original T5 50 & 34.82 & 19.93 & 32.62 & 3.85 \\ T5 50 with CL (ours) & 53.13 & 41.03 & 50.85 & 33.95 \\ Original T5 250 & 56.45 & 45.01 & 53.96 & 37.48 \\ T5 250 with CL (ours) & 59.41 & 47.93 & 56.16 & 38.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CSL results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **BLEU** \\ \hline RNN-Word & 17.7 & 8.5 & 15.8 & - \\ RNN-Char & 21.5 & 8.9 & 18.6 & - \\ RNN-context-Word & 26.8 & 16.1 & 24.1 & - \\ RNN-context-Char & 29.9 & 17.4 & 27.2 & - \\ mT5 & - & - & 34.8 & - \\ CPM-2 & - & - & 35.88 & - \\ \hline Original T5 200 & 23.61 & 12.00 & 21.80 & 3.99 \\ T5 200 with CL (ours) & 28.28 & 15.48 & 25.84 & 10.56 \\ Original T5 1000 & 28.01 & 15.59 & 25.66 & 9.51 \\ T5 1000 with CL (ours) & 30.09 & 18.59 & 29.00 & 14.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LCSTS results. models without unsupervised learning. This comparison sheds light on the effectiveness of incorporating unsupervised learning techniques in our approach, as evidenced by the improved summarization performance, particularly when well-paired data or \"gold batches\" were limited. Our semi-supervised method notably improved the performance across every metric compared to the fully supervised T5 model trained on a limited amount of labeled data. When labeled text pairs were extremely rare, the proposed method significantly improved the performance on every metric, especially the BLEU score (from 3.85 to 33.95 on SCL and from 3.99 to 10.56 on LCSTS). As the number of golden batches increased, the original T5 achieved better results, while our method still ameliorated its performance. This demonstrates the effectiveness of our approach in leveraging the information contained in unlabeled data. The present study showcases a portion of the experimental findings, which are visually presented in Figures 11 and 12.