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New method generates queries for summarization tasks from query-free datasets

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method to automatically generate relevant queries from existing query-free summarization datasets. This approach aims to create useful datasets for Query-Focused Summarization (QFS) tasks. Experiments show that summaries generated using these automatically created queries achieve performance comparable to those generated with original queries, suggesting the viability of this evidence-based query generation technique. AI

IMPACT This research could enable the creation of more specialized datasets for query-focused summarization, potentially improving performance in information retrieval and summarization systems.

RANK_REASON The cluster contains an academic paper detailing a new method for generating datasets for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method generates queries for summarization tasks from query-free datasets

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The cluster contains an academic paper detailing a new method for generating datasets for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yllias Chali, Deen Abdullah ·

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

    arXiv:2605.05392v1 Announce Type: new 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 rese…

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