QUARTZ : QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarization

• URL: http://arxiv.org/abs/2509.26302v1
• Date: Tue, 30 Sep 2025 14:16:08 GMT
• Title: QUARTZ : QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarization
• Authors: Mohamed Imed Eddine Ghebriout, Gaël Guibon, Ivan Lerner, Emmanuel Vincent,
• Abstract summary: app is a framework for task-oriented utility-based dialogue summarization.<n>app generates multiple summaries and task-oriented question-answer pairs from a dialogue in a zero-shot manner.<n>When validated on multiple datasets, app demonstrates its effectiveness by achieving competitive results.
• Score: 7.218694799833917
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dialogue summarization aims to distill the core meaning of a conversation into a concise text. This is crucial for reducing the complexity and noise inherent in dialogue-heavy applications. While recent approaches typically train language models to mimic human-written summaries, such supervision is costly and often results in outputs that lack task-specific focus limiting their effectiveness in downstream applications, such as medical tasks. In this paper, we propose \app, a framework for task-oriented utility-based dialogue summarization. \app starts by generating multiple summaries and task-oriented question-answer pairs from a dialogue in a zero-shot manner using a pool of large language models (LLMs). The quality of the generated summaries is evaluated by having LLMs answer task-related questions before \textit{(i)} selecting the best candidate answers and \textit{(ii)} identifying the most informative summary based on these answers. Finally, we fine-tune the best LLM on the selected summaries. When validated on multiple datasets, \app demonstrates its effectiveness by achieving competitive results in various zero-shot settings, rivaling fully-supervised State-of-the-Art (SotA) methods.

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