{"ID":2862814,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26302","arxiv_id":"2509.26302","title":"QUARTZ : QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarization","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.","short_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 res...","url_abs":"https://arxiv.org/abs/2509.26302","url_pdf":"https://arxiv.org/pdf/2509.26302v1","authors":"[\"Mohamed Imed Eddine Ghebriout\",\"Gaël Guibon\",\"Ivan Lerner\",\"Emmanuel Vincent\"]","published":"2025-09-30T14:16:08Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}