## 3 Results
### Comprehensive Performance Evaluation
#### 3.2.6 Semantic Quality Assessment with BERTScore  
While ROUGE metrics provide insights into lexical overlap, they may not fully capture semantic similarity in cross-lingual settings. To complement our evaluation, we compute BERTScore across all language pairs using XLM-RoBERTa, which provides contextual embeddings better suited for cross-lingual assessment. **MuRXLS consistently outperforms random sampling. ** Table 4 presents BERTScore results across all language pairs, comparing our retrieval-based example selection (MuRXLS) against random sampling for both GPT-4o and Llama-3-70B models. Our analysis shows that MuRXLS consistently outperforms random sampling across nearly all language pairs and models. The average improvement in BERTScore F1 ranges from +0.02 to +0.07 depending on the model and direction. This confirms that retrieval-based examples enhance both lexical overlap and semantic similarity, providing summaries that better preserve the meaning of source articles. Two-shot settings generally outperform one-shot configurations for MuRXLS. Two-shot settings generally outperform one-shot configurations for MuRXLS, aligning with our ROUGE findings. Interestingly, random sampling does not consistently benefit from additional examples, particularly in the EN\(\rightarrow\)X direction. **Directional asymmetry remains evident in both ROUGE and BERTScore. ** The directional asymmetry observed in ROUGE evaluation is also evident in BERTScore results, with X\(\rightarrow\)EN generally achieving higher semantic similarity than EN\(\rightarrow\)X across both models and methods. This persistent pattern suggests fundamental challenges in generating content in low-resource languages that retrieval-based methods help mitigate but cannot fully overcome. **BERTScore highlights moderate semantic similarity for English-Pashto despite low lexical overlap. ** Particularly notable is the case of English-Pashto. While ROUGE scores for this language pair were consistently near zero in the EN\(\rightarrow\)X direction, our BERTScore analysis reveals moderate semantic similarity (around 0.73 for GPT-4o with MuRXLS), indicating that generated summaries maintain semantic correspondence with references despite lacking direct lexical matches. This highlights BERTScore's ability to detect meaning preservation even when vocabulary choices differ substantially. **Three-shot configurations significantly increase computational costs without commensurate gains. ** Regarding experiment settings, as stated in Table 5, our analysis found that three-shot configurations significantly increase computational costs without corresponding performance gains. The average context length for three-shot plus query across all language pairs is over 4000 tokens, representing a consistent 50% increase in token consumption compared to two-shot settings. **Minimal performance improvements from three-shot usage justify focusing on up to two-shot settings. ** Our preliminary experiments demonstrated that this increased token usage rarely translated to performance improvements. For GPT-4o, three-shot settings only improved performance for 3 out of 12 language pairs with marginal gains. Meanwhile, GPT-3.5-turbo consistently underperformed in 10 out of 12 language pairs when using three examples. The performance impact was directionally asymmetric, with X\(\rightarrow\)EN language pairs showing slightly better resilience to the three-shot format compared to EN\(\rightarrow\)X pairs. Based on these findings, we focused our main experiments on zero-shot through two-shot settings, which provide the optimal balance between performance and computational efficiency.