## 3 Results
### Comprehensive Performance Evaluation
#### 3.2.9 Correlation Between Example Similarity and Summarization Performance  
To validate our hypothesis that semantically similar examples improve XLS performance, we analyze the relationship between example similarity and ROUGE scores. We compute the cosine similarity between each test article and its selected examples, then measure the correlation between these similarity scores and the resulting summarization performance.  
To validate our hypothesis that semantically similar examples lead to better XLS performance, we analyze the correlation between example similarity and ROUGE-L scores. For each test article, we measure the cosine similarity between its embedding and the embeddings of the selected examples, comparing how MuRXLS and random sampling approaches differ in selecting semantically relevant examples.  
As shown in Figure 3, we observe a significant difference in both similarity scores and their correlation with performance across the two translation directions. In the X\(\rightarrow\)EN direction, there is a strong positive correlation (r = 0.908) between example similarity and ROUGE-L scores, indicating that more semantically similar examples consistently lead to better summarization performance when translating from low-resource languages to English. The MuRXLS-selected examples (green circles) consistently show higher similarity scores (ranging from 55% to 65%) compared to random sampling examples (orange squares, ranging from 35% to 48%), which explains their superior performance.  
In contrast, the EN\(\rightarrow\)X direction exhibits a weaker correlation (r = 0.295) between similarity and performance. While MuRXLS still selects examples with higher similarity (54% to 61%) compared to random sampling (33% to 42%), this advantage translates less consistently to performance improvements. This weaker correlation helps explain the directional asymmetry observed in our main results: the benefits of retrieval-based  
Figure 3: Relationship between example similarity and ROUGE-L scores. Light green circles represent MuRXLS-selected examples, and light blue squares represent random sampling examples. The regression line shows a strong positive correlation (r = 0.908) in the X\(\rightarrow\)EN direction, while the EN\(\rightarrow\)X direction shows a weaker correlation (r = 0.295). This suggests that semantically similar examples have a stronger impact on summarization quality when translating from low-resource languages to English than vice versa.  
example selection are more pronounced when generating content in English than in low-resource languages.  
The strong correlation in the X\(\rightarrow\)EN direction suggests that when LLMs are tasked with summarizing content in a language they are well-trained on (English), the semantic relevance of examples has a substantial impact on their ability to extract and organize key information from the source article. Conversely, when generating content in low-resource languages, other factors such as the model's inherent language generation capabilities may limit the extent to which semantically relevant examples can improve performance.  
This analysis provides statistical evidence supporting our core hypothesis that intelligently selecting examples based on semantic similarity substantially improves cross-lingual summarization performance, particularly in the X\(\rightarrow\)EN direction. The consistent pattern across all language pairs demonstrates that the benefit of retrieval-based example selection represents a general principle for improving few-shot XLS rather than being specific to particular languages or models.