## 5 Conclusions  
In this paper, we introduced MuRXLS, a retrieval-based framework for cross-lingual summarization that dynamically selects semantically relevant examples for low-resource languages. By leveraging the BGE-M3 multilingual embedding model, our approach enhances few-shot learning for various LLMs including Mistral-7B, Llama-3-70B, GPT-3.5, and GPT-4o. Our experiments across twelve language pairs revealed a clear directional asymmetry: MuRXLS significantly outperformed random sampling in X\(\rightarrow\)English scenarios (with improvements up to 4.20 ROUGE-L points) while showing more modest gains in English\(\rightarrow\)X settings. Analysis of Correct Language Rate (CLR) demonstrated that our approach substantially improves language adherence in challenging target languages. The strong correlation (r = 0.908) between example similarity and summarization quality in the X\(\rightarrow\)English direction provides statistical evidence for our core hypothesis, while the weaker correlation in the reverse direction (r = 0.295) helps explain the observed asymmetry. These findings suggest that the benefits of retrieval-based selection are most pronounced when generating content in a high-resource language. While MuRXLS does not fully match dedicated fine-tuning in all scenarios, it offers a practical alternative that requires no language-specific training, making it valuable for low-resource languages with limited parallel data. Future Research Directions.The findings of this work open several promising avenues for advancing cross-lingual summarization and retrieval-based in-context learning methodologies. Future research should prioritize evaluation across a more linguistically diverse set of languages, particularly including representatives from major language families such as Niger-Congo (e.g., Swahili, Yoruba), Afro-Asiatic (e.g., Arabic, Amharic), and indigenous language families from the Americas and Pacific regions. Such expansion would provide stronger evidence for the universal applicability of retrieval-based approaches and reveal potential language-family-specific patterns in cross-lingual transfer effectiveness. Several technical directions could substantially improve the effectiveness of retrieval-based cross-lingual summarization. Developing ensemble approaches that combine multiple multilingual embedding models could enhance retrieval robustness and reduce language-specific biases. Dynamic example selection algorithms that adaptively determine the optimal number of examples based on query complexity and available context length represent another promising direction. Furthermore, exploring domain-specific embedding models trained on specialized corpora (scientific, legal, medical) could extend the applicability of our framework beyond news summarization. Future work should also focus on developing more sophisticated evaluation metrics that better capture semantic preservation across languages, potentially incorporating human evaluation protocols specifically designed for cross-lingual content assessment. The application of our framework to real-world scenarios such as multilingual information retrieval systems, cross-lingual document indexing, and educational content generation for low-resource languages presents significant practical opportunities. These research directions collectively aim to enhance the accessibility, effectiveness, and practical applicability of cross-lingual summarization technologies, particularly for underserved language communities that would benefit most from improved multilingual information access capabilities. Conceptualization, G.P. and H.L. ; methodology, G.P. and H.L. ; software, G.P. ; validation, G.P., J.P. and H.L. ; formal analysis, G.P. and J.P.; investigation, G.P. and J.P.; resources, H.L. ; data curation, G.P. ; writing--original draft preparation, G.P. and J.P.; writing--review and editing, G.P., J.P. and H.L. ; visualization, G.P. ; supervision, H.L. ; project administration, H.L. ; funding acquisition, H.L.