## 4 Experiments
### Results  
In this section, we present the results of our proposed approach for automatic text summarization and compare its performance with baselines on four commonly used evaluation metrics: the ROUGE-1, ROUGE-2, ROUGE-L [37], and BLEU [38] scores. ROUGE is the acronym for Recall-Oriented Understudy for Gisting Evaluation, and BLEU is the acronym for BiLingual Evaluation Understudy. The evaluation metrics play a critical role in assessing the effectiveness of a summarization model. The ROUGE and BLEU scores are widely used to evaluate the quality of generated summaries. ROUGE measures the overlap between the generated summary and the reference summary at the n-gram level, whereas BLEU assesses the quality of the summary by computing the n-gram precision between the generated summary and the reference summary. By comparing the performance of our proposed model with the baselines on these four metrics, we can determine the effectiveness of our approach in automatic text summarization. To provide clarity, we present the formal definitions of these metrics as follows:  
\[ROUGE\text{-}N=\frac{\sum_{S\in\{ReferenceSummaries\}}\sum_{gram_{n}\in S}Count _{match}(gram_{n})}{\sum_{S\in\{ReferenceSummaries\}}\sum_{gram_{n}\in S}Count (gram_{n})} \tag{5}\]  
where n stands for the length of the n-gram, \(gram_{n}\), and \(Count_{match}(gram_{n})\) is the maximum number of n-grams co-occurring in a candidate summary and a set of reference summaries. By switching the reference and summary, we get the precision and recall values. The final ROUGE-N score is, hence, the F1 score. We used ROUGE-1 and ROUGE-2 in our experiments. ROUGE-L is based on the longest common subsequence (LCS). It is calculated in the same way as ROUGE-N, but by replacing the n-gram match with the LCS. \[BLEU=BP\cdot\exp(\sum_{n=1}^{N}w_{n}\log p_{n}) \tag{6}\]  
where \(p_{n}\) is the proportion of correctly predicted n-grams within all predicted n-grams. Typically, we use \(N=4\) kinds of grams and uniform weights \(w_{n}=N/4\). BP is the brevity penalty, which penalizes sentences that are too short:  
\[\text{Brevity Penalty}\ =\left\{ \begin{array}{ll}1,&\text{if }c>r\\ e^{(1-r/c)},&\text{if }c<=r\end{array} \right. \tag{7}\]  
where \(c\) is the predicted length and \(r\) is the target length. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Optimizer & AdamW \\ \hline Learning rate & \(5\times 10^{-5}\) \\ \hline \(\beta 1\) & 0.9 \\ \hline \(\beta 2\) & 0.999 \\ \hline \(\epsilon\) & \(1\times 10^{-6}\) \\ \hline Weight decay & 0.01 \\ \hline Learning rate schedule & Cosine decay \\ \hline Sentence length & 512 tokens \\ \hline Batch size & 8 \\ \hline Identity mapping loss weight & 0.1 \\ \hline Cycle consistency loss weight & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters used to train the model. We conducted experiments on two Chinese datasets: CSL [33], which consists of abstracts from the scientific literature and their corresponding titles, and LCSTS [34], which consists of Chinese news articles and their corresponding human-written summaries. Due to the lack of research on semi-supervised Chinese summarization, all baselines used in this study were fully supervised models and were proposed by the organizers of the original corresponding datasets. For the CSL dataset, we conducted the supervised part of the experiment with two fractions of the original dataset: one using 50 paired samples, and the other using 250, while the remaining data were used for the unsupervised part of our method. For the LCSTS dataset, which was larger than CSL, we conducted the experiments with 200 and 1000 paired samples. We also performed an ablation study in comparison with the T5 model trained with labeled data only and without our proposed loss functions. The T5 models in Table 2 refer to the results obtained in these cases. Table 2 illustrates the performance of the baselines and our proposed approach on the CSL dataset, while Table 3 shows the results on the LCSTS dataset.