arxiv: 2605.05392 · v1 · submitted 2026-05-06 · 💻 cs.CL · cs.AI

Recognition: unknown

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

Deen Abdullah, Yllias Chali

Authors on Pith no claims yet

Pith reviewed 2026-05-08 16:18 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords query-focused summarizationquery generationdataset creationROUGE evaluationevidence-based queriessummarization models

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The pith

An evidence-based model can generate queries from query-free summarization datasets that enable competitive query-focused summarization performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large summarization datasets often lack the queries needed for query-focused summarization tasks. This paper develops a method to automatically create evidence-based query keywords from documents and their summaries alone. The authors test whether these generated queries resemble the original ones in existing QFS datasets and whether they support good summarization results. Experiments with multiple pre-trained models show that summaries produced using the generated queries reach ROUGE scores similar to those from the original queries. If successful, this would allow many existing datasets to be adapted for training query-focused summarizers.

Core claim

The central discovery is a model for generating queries from query-free data by focusing on evidence present in the documents and summaries. Intrinsic tests measure similarity to human-provided queries in two datasets. Extrinsic tests run summarization with various models including a state-of-the-art QFS system and find that evidence-based queries yield competitive ROUGE scores to the originals.

What carries the argument

Evidence-based query generation model that extracts keywords supported by the input document and reference summary.

If this is right

Generated queries produce summaries with ROUGE scores close to those from original queries.
The method works across different pre-trained summarization models and a SOTA QFS model.
Query-free datasets can be converted into resources suitable for query-focused summarization.
Intrinsic similarity checks confirm the generated queries align with original ones on tested data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This could greatly increase the amount of training data available for QFS models by repurposing general summarization corpora.
The approach might be extended to generate queries for other tasks like question answering from existing datasets.
Testing on more diverse or real-world queries could reveal if the evidence-based property holds beyond ROUGE metrics.

Load-bearing premise

That achieving competitive ROUGE scores with generated queries on the evaluated datasets and models indicates they are generally effective for query-focused summarization.

What would settle it

A follow-up experiment where summaries using the generated queries show clearly inferior ROUGE scores compared to original queries on a new dataset or model.

Figures

Figures reproduced from arXiv: 2605.05392 by Deen Abdullah, Yllias Chali.

**Figure 1.** Figure 1: Evidence Model - Fine-tuning T5 in CNN/DM view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows a workable way to generate queries from plain doc-summary pairs and gets competitive ROUGE on existing QFS sets, but the tests never leave those sets so generalization to real query-free inputs stays unproven.

read the letter

This paper shows how to train a model on ordinary document-summary pairs to produce queries, then checks that those queries yield ROUGE scores close to human ones when fed to summarizers on two standard QFS datasets. The intrinsic check measures overlap with the gold queries already present in those sets. The extrinsic check runs several pre-trained models plus one SOTA QFS system and reports that the generated queries perform at competitive levels. That supplies a concrete augmentation route for QFS data, which is a real practical need. The approach is new in its specific evidence-based framing and the dual intrinsic-extrinsic setup. The numbers line up with what the abstract claims, and nothing in the described pipeline looks circular on its face. The main soft spot is that every evaluation stays inside QFS collections that already contain human queries. The generator is never run on a fresh document where no query or summary exists, so the ROUGE results do not yet show whether the same model would produce useful queries in the wild. If training lets any summary-derived signal leak into query prediction, the competitive scores could overstate real-world utility. The abstract is light on architecture, training details, and statistical tests, though the full text presumably supplies them. Readers who build or expand QFS datasets will get direct value from the method as a starting point. It is incremental rather than foundational, but the work is clear enough and the results are reproducible enough to merit referee time. I would send it for peer review with the expectation that reviewers will ask for experiments on genuinely query-free corpora.

Referee Report

1 major / 2 minor

Summary. The paper proposes an evidence-based model to generate queries from query-free document-summary pairs and evaluates it on two QFS datasets. Intrinsically, it measures similarity between generated and gold queries; extrinsically, it feeds the generated queries into various summarization models (including a SOTA QFS model) and reports that the resulting ROUGE scores are competitive with those obtained using the original gold queries.

Significance. If the evaluation holds under more rigorous testing, the work would be significant for QFS research by providing a way to convert abundant query-free summarization corpora into query-focused ones, mitigating data scarcity without requiring new human annotations.

major comments (1)

[Evaluation] Evaluation section: The extrinsic evaluation (ROUGE comparisons) and intrinsic similarity checks are performed exclusively on existing QFS datasets that already contain human-annotated queries. The generator is never applied to a genuinely query-free corpus (where no gold query exists for reference), so the competitive ROUGE scores do not demonstrate that the generated queries would be effective for downstream QFS on new, query-free documents.

minor comments (2)

[Abstract] Abstract and Methods: The manuscript provides no details on the query generator's architecture, training data sources, hyperparameters, or exact QFS datasets used, which hinders assessment of reproducibility and potential confounds.
[Results] Results: No statistical significance tests or variance estimates are reported for the ROUGE score differences, making it unclear whether the 'competitive' performance is reliably equivalent to the gold-query baseline.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the thoughtful review and for highlighting an important aspect of our evaluation design. We address the major comment below.

read point-by-point responses

Referee: [Evaluation] Evaluation section: The extrinsic evaluation (ROUGE comparisons) and intrinsic similarity checks are performed exclusively on existing QFS datasets that already contain human-annotated queries. The generator is never applied to a genuinely query-free corpus (where no gold query exists for reference), so the competitive ROUGE scores do not demonstrate that the generated queries would be effective for downstream QFS on new, query-free documents.

Authors: We agree that direct application to a corpus lacking any gold queries would provide stronger evidence for generalization to truly query-free settings. Our evaluation deliberately uses existing QFS datasets to enable controlled intrinsic (query similarity) and extrinsic (ROUGE) comparisons against human-annotated references, which serves as a rigorous proxy for the utility of the generated queries. To address the concern, we will revise the manuscript to add an experiment on a query-free summarization corpus (such as CNN/DailyMail). We will generate queries from document-summary pairs, feed them into the same summarization models, and report ROUGE scores of the resulting summaries against the human reference summaries, thereby demonstrating effectiveness without relying on gold queries. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation chain is self-contained with independent benchmarks

full rationale

The paper trains an evidence-based query generator exclusively on query-free document-summary pairs, then applies it to separate QFS datasets solely for evaluation. Intrinsic similarity to human queries and extrinsic ROUGE comparisons on those held-out QFS datasets do not reduce any claimed prediction to the training inputs by construction, nor rely on self-citations or fitted parameters from the evaluation set. The two research questions are addressed via standard transfer evaluation without tautological redefinition of results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No specific free parameters, axioms, or invented entities can be identified from the abstract alone.

pith-pipeline@v0.9.0 · 5447 in / 1013 out tokens · 56944 ms · 2026-05-08T16:18:26.142972+00:00 · methodology

discussion (0)

Reference graph

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