QFS-Composer: Query-focused summarization pipeline for less resourced languages

TL;DR

This paper introduces QFS-Composer, a novel query-focused summarization framework tailored for less-resourced languages, combining query decomposition, question generation, question answering, and abstractive summarization to enhance summary relevance.

Contribution

The work presents a new QFS pipeline for low-resource languages, including Slovenian-specific models and evaluation methods, demonstrating improved summary quality over baseline LLMs.

Findings

QA-guided summarization improves consistency and relevance.

Developed Slovenian QA and QG models based on Slovene LLM.

Established an extensible methodology for QFS in less-resourced languages.

Abstract

Large language models (LLMs) demonstrate strong performance in text summarization, yet their effectiveness drops significantly across languages with restricted training resources. This work addresses the challenge of query-focused summarization (QFS) in less-resourced languages, where labeled datasets and evaluation tools are limited. We present a novel QFS framework, QFS-Composer, that integrates query decomposition, question generation (QG), question answering (QA), and abstractive summarization to improve the factual alignment of a summary with user intent. We test our approach on the Slovenian language. To enable high-quality supervision and evaluation, we develop the Slovenian QA and QG models based on a Slovene LLM and adapt evaluation approaches for reference-free summary evaluation. Empirical evaluation shows that the QA-guided summarization pipeline yields improved consistency and relevance over baseline LLMs. Our work establishes an extensible methodology for advancing QFS in less-resourced languages.