[2605.05392] Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets
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Computer Science > Computation and Language
arXiv:2605.05392 (cs)
[Submitted on 6 May 2026]
Title:Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets
Authors:Yllias Chali, Deen Abdullah
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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.
Comments: 7 pages, 1 figure
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.05392 [cs.CL]
(or arXiv:2605.05392v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.05392
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yllias Chali [view email] [v1] Wed, 6 May 2026 19:25:34 UTC (58 KB)
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