NPRIP: Nucleus-to-Periphery Retrieval-Iterative Prompting for Improved Abstractive Summarization in Low-Resource Mongolian

Abstract

Large language models often face challenges in low-resource agglutinative language text summarization tasks due to poorly designed prompts, leading to core information dilution, reduced fidelity, and critical information loss caused by the complex grammatical structures of agglutinative languages. For traditional Mongolian, a typical low-resource agglutinative language, this paper proposes a Nucleus-to-Periphery Retrieval-Iterative Prompting (NPRIP). This method first guides the model to extract highly condensed semantic core information (events, persons, time, etc.) from the original text. Subsequently, through multiple rounds of self-refinement iteration, it progressively expands peripheral details (background, causes, consequences, secondary facts, etc.). The model performs fact consistency checks, redundancy removal, and fidelity correction on the current draft, achieving gradual improvements in information completeness and fidelity. To enhance Mongolian language representation, we perform parameter-efficient fine-tuning on LLaMA3-8B using the CCMT2019 Mongolian-Chinese parallel corpus, and construct a larger abstractive Mongolian news summarization dataset MoSum along with its augmented version. Experiments on traditional Mongolian text summarization tasks demonstrate that our proposed method significantly outperforms multiple baseline models on automatic evaluation metrics including ROUGE-1, ROUGE-2, and ROUGE-L. This validates the effectiveness of core-priority structured iterative prompting in low-resource agglutinative language summarization scenarios.