## 2 Methods
### Dataset and Language Selection
#### 2.1.2 Language Selection  
Six low-resource languages were systematically selected based on multiple methodological considerations to ensure robust evaluation across different language families and typological structures.  
For resource availability assessment, all selected languages meet low-resource criteria while ensuring sufficient data for meaningful evaluation. Each language has approximately 1000 article-summary pairs in the training corpus, which provides adequate statistical power for evaluation while maintaining low-resource characteristics. These languages have limited representation in multilingual LLM training data compared to high-resource languages and insufficient data for robust fine-tuning of large models, which typically requires over 10,000 examples.  
Importantly, these six languages represent the optimal subset from CrossSum that balances data adequacy with low-resource characteristics. Other languages in the dataset either had significantly fewer pairs (typically \(<\) 500), making them unsuitable for reliable statistical evaluation, or exceeded low-resource thresholds. This constraint-based selection ensures that our evaluation is both statistically valid and representative of genuine low-resource scenarios.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Pipeline** & **End-to-End** & **Random ICL** & **MuRXLS** \\  & **Methods** & **Neural** & **Methods** & **(Ours)** \\ \hline Semantic Example Selection & \(\times\) & \(\times\) & \(\times\) & ✓ \\ Cross-lingual Retrieval & \(\times\) & \(\times\) & \(\times\) & ✓ \\ No Task-specific Training & \(\times\) & \(\times\) & ✓ & ✓ \\ Directional Analysis & \(\times\) & \(\times\) & \(\times\) & ✓ \\ Low-resource Effectiveness & Low & Low & Medium & High \\ Computational Efficiency & Low & High & High & High \\ \hline \hline \end{tabular} \(\gamma\): supported; \(\times\): not supported.  
\end{table}
Table 1: Comparison of cross-lingual summarization approaches.  
Data quality validation was applied using specific metrics: minimum article length of 100 tokens, summary length ratio between 0.1-0.4 of source article, language detection confidence above 0.95 for both source and target, and content preservation score above 0.7 based on cross-lingual semantic similarity. The selection of these specific language pairs is methodologically crucial, as linguistic distance directly impacts embedding space alignment and retrieval effectiveness in our similarity calculation framework.  
For linguistic diversity, we selected languages from different families: Gujarati and Marathi from the Indo-Aryan branch of Indo-European languages, Thai and Burmese from the Sino-Tibetan family with distinct scripts and tonal systems, Pashto from Indo-Aryan with Arabic script and different morphological patterns, and Sinhala from Sino-Tibetan with a unique script system. This diversity ensures comprehensive testing of our framework across varied linguistic structures.  
Regarding typological distance from English, we chose languages with varying degrees of structural difference to assess cross-lingual transfer robustness. The selected languages exhibit diverse script systems including Devanagari (Gujarati, Marathi), Thai script, Burmese script, Arabic script (Pashto), and Sinhala script, with no Latin script representation. Word order patterns vary from SVO (Thai) to SOV (Gujarati, Marathi, Pashto) to mixed patterns (Burmese, Sinhala). Morphological complexity ranges from agglutinative to inflectional to isolating tendencies.