Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets

• URL: http://arxiv.org/abs/2605.05392v1
• Date: Wed, 06 May 2026 19:25:34 GMT
• Title: Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets
• Abstract summary: This paper proposes an evidence-based model to generate queries from query-free datasets.<n>We also perform summarization tasks using different pre-trained models, as well as a state-of-the-art (SOTA) QFS model.
• Score: 3.9103337761169943
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large-scale datasets are widely used to perform summarization tasks, but they may not include queries alongside documents and summaries. In the search for suitable datasets for Query-Focused Summarization (QFS), we identify two research questions: Is it possible to automatically generate evidence-based query keywords from query-free datasets? Does evidence-based query generation support the QFS task? This paper proposes an evidence-based model to generate queries from query-free datasets. To evaluate our model intrinsically, we compare the similarity between the original queries and the system-generated queries of two QFS datasets. We also perform summarization tasks using different pre-trained models, as well as a state-of-the-art (SOTA) QFS model, to measure the extrinsic performance of our query generation approach. Experimental results indicate that summaries generated using evidence-based queries achieve competitive ROUGE scores compared to those generated from the original queries.

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