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

📰 ArXiv cs.AI

Learn to generate query-focused summarization datasets from query-free datasets using evidence-based query keywords

advanced Published 9 May 2026

Action Steps

Identify query-free summarization datasets to convert
Apply evidence-based model to generate query keywords from documents and summaries
Evaluate generated query keywords for relevance and accuracy
Use generated query keywords to create query-focused summarization datasets
Test and fine-tune the resulting datasets for QFS tasks

Who Needs to Know This

NLP researchers and data scientists can benefit from this technique to create targeted summarization datasets for query-focused summarization tasks

Key Insight

💡 Evidence-based query generation can support query-focused summarization tasks

Key Takeaways

Learn to generate query-focused summarization datasets from query-free datasets using evidence-based query keywords

Full Article

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

Abstract:
arXiv:2605.05392v1 Announce Type: cross 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

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