Prilagajanje velikih jezikovnih modelov za ciljno povzemanje besedil v jezikih z manj viri

State-of-the-art large language models demonstrate strong performance in text summarization, yet their effectiveness varies significantly across languages with restricted training resources. This work addresses the challenge of query-focused summarization in Slovene, a language with limited availability of labeled datasets and evaluation tools. We present a novel query-focused summarization (QFS) framework, QFS-Composer, which integrates query decomposition, question generation (QG), question answering (QA), and abstractive summarization to increase factual alignment of a summary with user intent. To enable high-quality supervision and evaluation, we develop the Slovenian QA and QG models based on large language model (LLM) GaMS-9B-Instruct, and adapt evaluation approaches for reference-free summary evaluation in the Slovenian language. Experimental results show that the QA-guided summarization pipeline yields improved consistency and relevance over baseline LLMs. This research establishes an extensible methodology for advancing QFS in less-resourced languages.