LLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText

Heba H. Elwazzan; Krishna Chaitanya Marturi

REVIEW 3 major objections 2 minor 1 cited by

LLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read If the paper is right, scientific-text simplification is best done as two LLM passes: first plan or summarize, then rewrite.

desk verdict The abstract describes a modest CLEF shared-task system, but the supplied full text is a different, corrupted paper—nothing here can be evaluated. read the letter →

arxiv 2508.11816 v1 pith:ZYHBCCSN submitted 2025-08-15 cs.CL

Krishna Chaitanya Marturi , Heba H. Elwazzan This is my paper

classification cs.CL

keywords scientifictextsimplificationlargelanguagemodelspromptengineeringtwo-stagegenerationplan-basedrewritingsummarization-guidedcoherencesharedtasksystem

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper describes a two-stage LLM approach to simplifying scientific text. At the sentence level, an LLM first generates a structured plan—what to keep, what to cut, how to rephrase—and then rewrites the sentence according to that plan. At the document level, an LLM first produces a concise summary and then uses that summary to guide simplification of the full text. The paper's claim is that inserting this intermediate planning/summarizing step makes the final simplifications more coherent and more faithful to the original meaning than rewriting the source directly in one pass. If that claim holds, the approach offers a lightweight, prompt-only way to improve LLM text simplification.

What carries the argument

The central mechanism is the two-stage prompt chain. At sentence level the load-bearing object is the structured plan: a model-generated list of simplification actions or target properties that is fed back into a second prompt to constrain the rewrite. At document level it is the generated summary, which supplies global context so individual sentence simplifications stay consistent with the document's main points. The work these objects do is to separate content selection from surface rewriting, so the second stage has explicit guidance rather than inferring intent from the source text alone.

What would settle it

Run a controlled comparison on the same scientific sentences and documents: system A uses the two-stage plan/summary pipeline, system B rewrites directly in one LLM call. If human raters or automated faithfulness metrics find B equal to or better than A on coherence and meaning preservation, the central claim is falsified. A second check is whether deliberately removing the plan/summary from the prompt—keeping the same two calls but filling the middle step with irrelevant text—produces no quality drop.

Watch

Extended reading notes

Core claim

The central claim is that a two-stage, LLM-guided pipeline improves both sentence-level and document-level simplification of scientific writing. For each sentence, the pipeline prompts an LLM to produce a structured plan before any rewriting; for each document, it prompts an LLM to produce a concise summary and then conditions the simplification on that summary. The authors' position is that these intermediate artifacts force the model to decide what is essential before it changes the words, yielding output that is more coherent and contextually faithful. The contribution is the pipeline design itself and the prompting strategy, not a new model or training procedure.

Load-bearing premise

The pipeline assumes the plan and the summary preserve all essential content and relationships from the source, so that any information lost in those intermediate artifacts cannot be recovered by the later simplification step.

Editorial extensions

If this is right

Simplification becomes controllable: editors could inspect or edit the plan before the rewrite is generated.
The same two-step pattern can be applied to other rewriting tasks where fidelity matters, such as legal or health-text simplification.
Document-level summary guidance should reduce cross-sentence inconsistency in simplified outputs.
The approach requires no fine-tuning or new training data, so it can be deployed with any instruction-following LLM.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If plan quality is the bottleneck, then an explicit verification step—checking that the plan preserves key terms before rewriting—would likely strengthen the pipeline; the paper does not propose this step.
The summary-guided variant could be tested against a 'summary-free' document-level baseline; I would expect the gap to widen on long documents with many coreference chains.
A natural stress test is to corrupt the plan or summary deliberately and measure how much the final simplification degrades; that would reveal whether the two stages are actually doing causal work or merely adding verbosity.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: I can't review this in any meaningful sense. The abstract outlines a two-stage LLM prompting pipeline for the CLEF SimpleText task—first generate a sentence-level plan, then simplify; at document level, generate a summary and use it to guide simplification. That's a reasonable shared-task system description, and if the authors included results and ablations, it might be a useful incremental contribution for people working on scientific text simplification.

But the full text under this arXiv ID does not match the abstract. The decodable portions are a statistics paper about simulation-based calibration, deep adaptive proposals, and binomial calibration, with figures captioned 'Flipped - SBC', 'Noise - DAP', 'Bias - Bin. calib.' and equations referencing 'model log_lik var_y'. There is no mention of LLMs, simplification, planning, summaries, or CLEF SimpleText anywhere in the body. Most of the text is corrupted mojibake, but what survives is clearly a different paper. So the central claim in the abstract—that the two-stage framework improves coherence and faithfulness—is supported by no visible method or evaluation.

To be fair, the abstract itself is honest in scope: it asserts the approach 'enables' more coherent simplifications, which is the kind of claim that needs a direct comparison against plain LLM rewriting. There are no quantitative results, no baselines, no details on the LLM used, prompts, or eval metrics. Even if the full text were intact, this would be a thin system description for a shared task. As it stands, the submission is not reviewable.

The reader's concern about information preservation in plans/summaries is a reasonable empirical question, but it presupposes the method is actually described somewhere. It isn't here.

My recommendation: desk reject. If the authors uploaded the wrong PDF, they should resubmit the correct one; but the version I have gives both an editor and a referee nothing to engage with. I would not send this to peer review, and I would not cite it.

Referee Report

3 major / 2 minor

Summary. The abstract describes a system submitted to CLEF 2025 SimpleText Task 1 for scientific text simplification. The sentence-level method uses LLMs to first generate a structured plan and then performs plan-driven simplification; the document-level method uses LLM-generated summaries to guide simplification. The abstract claims that this two-stage, LLM-based framework produces more coherent and contextually faithful simplifications. However, the supplied full text is heavily corrupted (mojibake) and its decodable portions concern a completely different topic—simulation-based calibration (SBC), deep adaptive proposals (DAP), and binomial calibration—with figures such as 'Flipped - SBC', 'Noise - DAP', and 'Bias - Bin. calib.'. The body contains no description of the proposed simplification system, no data, no baselines, and no evaluation results. The central claim is therefore supported only by the abstract and is unverifiable from the manuscript as submitted.

Significance. If the claimed two-stage plan-then-simplify and summarize-then-simplify framework were properly evaluated, it could be a useful contribution to scientific text simplification, a topic of direct relevance to the CLEF SimpleText shared task. The paper may also serve as a system description for that task. However, the manuscript currently provides no verifiable support for its central claim: there is no method description, no experimental setup, no comparison to direct rewriting baselines, and no quantitative or qualitative results. No code, data, or machine-checked artifacts are provided. The significance of the contribution cannot be assessed from the submitted text.

major comments (3)

[Full text (entire body)] The supplied full text is unrelated to the abstract. The abstract describes LLM-based planning and summarization for scientific text simplification, but the decodable portions of the body concern simulation-based calibration, deep adaptive proposals, and binomial calibration (e.g., figure captions 'Flipped - SBC', 'Noise - DAP', 'Bias - Bin. calib.', and equations referencing 'model log_lik var_y'). There is no description of the proposed two-stage framework, no details of the CLEF SimpleText task setup, no baselines, and no evaluation. The central claim is therefore unsupported by any evidence in the manuscript body.
[Abstract, final sentence] The abstract states: 'This two-stage, LLM-based framework enables more coherent and contextually faithful simplifications of scientific text.' This is an empirical comparative claim, but the paper reports no experiments, no metrics (e.g., readability, faithfulness, SARI or SimpleText-specific scores), and no comparison against direct LLM simplification. Without any evaluation, this claim is not verifiable and should be either removed or substantiated with results.
[Method description (absent)] Even setting aside the corruption, the paper does not describe the prompts, the structure of the 'plan', how summaries are generated, how they 'guide' simplification, or how the sentence-level and document-level modules interact. There is also no discussion of potential error propagation from the plan/summary stage to the simplification stage—a key risk given that the abstract implies information is preserved in those intermediate representations. This missing method description prevents reproduction or even partial assessment of the approach.

minor comments (2)

[Full text encoding] The full text is severely corrupted (mojibake) with unreadable characters throughout. The authors should ensure the correct PDF or source file is submitted, as the current file is not reviewable.
[Related work / references] The abstract does not cite any prior work on text simplification, LLM-based planning, or the SimpleText shared task. If a corrected manuscript is submitted, it should include appropriate references to the CLEF SimpleText task description and related simplification literature.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the full text is unrelated to the abstract, so the central claim is unsupported but not circular.

full rationale

The paper's abstract claims that a two-stage, LLM-based planning/summarizing framework improves coherence and contextual faithfulness for scientific text simplification. However, the supplied full text is largely garbled and, where decodable, concerns an entirely different topic: simulation-based calibration (SBC), deep adaptive proposals (DAP), binomial calibration, and related statistical diagnostics, with figure captions such as 'Flipped - SBC', 'Noise - DAP', and 'Bias - Bin. calib.' and equations referencing 'model log_lik var_y'. I searched for any derivation chain in which a claimed prediction or first-principles result reduces by construction to its own inputs. None exists: there are no fitted parameters renamed as predictions, no self-citation used as load-bearing evidence, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via citation that defines the target result. The mismatch between abstract and body is a serious verifiability and completeness problem, but it is not a circularity problem. The comparative claim about simplification quality is empirically checkable in principle and is not equivalent to the method's definition. Under the hard rules, circularity may only be flagged with quoted equations or explicit self-citational reductions; no such reduction is present. Therefore the correct finding is no significant circularity, score 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No numbers fitted, no new entities introduced in the abstract. The framework's unstated assumptions are about the reliability of the LLM's plan and summary outputs; these are empirical premises that the paper would need to support with evaluation and baselines.

assumptions (3)

domain assumption LLM-generated structured plans preserve the essential content and constraints of source sentences.
The sentence-level pipeline assumes planning does not introduce or lose content before simplification occurs (abstract, sentence-level stage).
domain assumption LLM-generated document summaries preserve the information needed for faithful simplification.
The document-level pipeline assumes summaries retain the key scientific content, so simplification guided by summaries remains contextually faithful (abstract, document-level stage).
domain assumption Off-the-shelf LLMs can perform the planning, summarization, and simplification steps adequately.
The framework gives no training or fine-tuning in the abstract; it relies on the base model's instruction-following ability.

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Cite this review

Pith. "Pith review of LLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText." pith.science (2026). https://pith.science/paper/ZYHBCCSN

@misc{pith2026250811816,
  author       = {Pith},
  title        = {Pith review of: LLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZYHBCCSN}},
  note         = {Machine review of arXiv:2508.11816}
}

read the original abstract

In this paper, we present our approach for the CLEF 2025 SimpleText Task 1, which addresses both sentence-level and document-level scientific text simplification. For sentence-level simplification, our methodology employs large language models (LLMs) to first generate a structured plan, followed by plan-driven simplification of individual sentences. At the document level, we leverage LLMs to produce concise summaries and subsequently guide the simplification process using these summaries. This two-stage, LLM-based framework enables more coherent and contextually faithful simplifications of scientific text.

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

13 extracted references · 7 canonical work pages · cited by 1 Pith paper

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Reviewed August 5, 2026 · model on record in the stance chip above.

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Saggion , title Automatic Text Simplification , publisher Springer International Publishing , address Cham , year 2022

author H. Saggion , title Automatic Text Simplification , publisher Springer International Publishing , address Cham , year 2022 . https://link.springer.com/book/10.1007/978-3-031-02166-4. :10.1007/978-3-031-02166-4

doi:10.1007/978-3-031-02166-4 2022

[2] [2]

Ermakova , author et al

author L. Ermakova , author et al. , title Overview of clef 2025 simpletext track: Simplify scientific texts (and nothing more) , in: editor J. Carillo de Albornoz , editor et al. (Eds.), booktitle Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Fifteenth International Conference of the CLEF Association (CLEF 2025...

work page 2025

[3] [3]

Bakker , author et al

author J. Bakker , author et al. , title Overview of the clef 2025 simpletext task 1: Simplify scientific text , in: editor G. Faggioli , editor et al. (Eds.), booktitle Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2025) , CEUR Workshop Proceedings, publisher CEUR-WS.org , year 2025

work page 2025

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Bakker , author J

author J. Bakker , author J. Kamps , title Cochrane-auto: An aligned dataset for the simplification of biomedical abstracts , in: editor M. Shardlow , editor H. Saggion , editor F. Alva-Manchego , editor M. Zampieri , editor K. North , editor S. Štajner , editor R. Stodden (Eds.), booktitle Proceedings of the Third Workshop on Text Simplification, Accessi...

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Ondov , author K

author B. Ondov , author K. Attal , author D. Demner-Fushman , title A survey of automated methods for biomedical text simplification , journal Journal of the American Medical Informatics Association volume 29 ( year 2022 ) pages 1976--1988 . :10.1093/jamia/ocac149

work page doi:10.1093/jamia/ocac149 2022

[6] [6]

Guidroz , author D

author T. Guidroz , author D. Ardila , author J. Li , author A. Mansour , author P. Jhun , author N. Gonzalez , author X. Ji , author M. Sanchez , author S. Kakarmath , author M. M. Bellaiche , author M. Ángel Garrido , author F. Ahmed , author D. Choudhary , author J. Hartford , author C. Xu , author H. J. S. Echeverria , author Y. Wang , author J. Shaff...

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Progressive Document-level Text Simplification via Large Language Models

author D. Fang , author J. Qiang , author Y. Zhu , author Y. Yuan , author W. Li , author Y. Liu , title Progressive document-level text simplification via large language models , year 2025 . https://arxiv.org/abs/2501.03857. http://arxiv.org/abs/2501.03857 arXiv:2501.03857 , note preprint

work page Pith review arXiv 2025

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Xu , author C

author W. Xu , author C. Napoles , author E. Pavlick , author Q. Chen , author C. Callison-Burch , title Optimizing statistical machine translation for text simplification , journal Transactions of the Association for Computational Linguistics volume 4 ( year 2016 ) pages 401--415 . https://aclanthology.org/Q16-1029/. :10.1162/tacl_a_00107

doi:10.1162/tacl_a_00107 2016