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The Use of Readability Metrics in Legal Text: A Systematic Literature Review

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A systematic review finds 16 readability metrics used in legal texts, with Flesch-Kincaid Grade Level the most common.

desk verdict A useful first map of a scattered field, but the numbers in the tables are not internally consistent enough to be cited until the review is revised. read the letter →

arxiv 2411.09497 v1 pith:Q3ZT6MT2 submitted 2024-11-14 cs.CL

Yu Han , Aaron Ceross , Jeroen H.M. Bergmann This is my paper · ORCID
classification cs.CL
keywords readabilitymetricslegaltextsystematicreviewFlesch-KincaidGradeLevelinformedconsentformsregulatorycomplexityplainlanguagelinguistic
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 systematic review sets out to establish, for the first time, which readability metrics researchers actually apply to legal and regulatory texts, and where they apply them. Screening 3,566 records from three databases, the authors narrow the field to 34 studies and find 16 distinct metrics. Their central descriptive result is that the Flesch-Kincaid Grade Level is the most frequently used metric, followed by the Flesch Reading Ease score and SMOG, and that 73.5% of the studies examine informed consent forms. The paper concludes that legal readability measurement is concentrated in a narrow medical niche and lacks any consensus standard, which matters because overly complex laws are hard for the public to engage with and because NLP tools for legal text need a common readability baseline.

What carries the argument

The load-bearing object is the inventory itself: a table of 34 studies, each tagged with the method it applied and the legal subdomain it studied, assembled through a structured literature-screening protocol with two independent reviewers and a third for disagreements. The named formulas that carry the counting argument include the Flesch-Kincaid Grade Level (F-KGL), which converts average words per sentence and syllables per word into a U.S. school-grade score, the Flesch Reading Ease score (FRES), a 0-100 scale with higher meaning easier, and SMOG, which counts polysyllabic words in sampled sentences. These formulas are the units being counted, and the inventory is what transforms them into the frequency claims.

What would settle it

Re-run the three-database search with the same keyword combination and inclusion criteria, then have two independent teams apply the criteria to the full texts; if the reproduced set does not include the same 34 studies, or if judges disagree about entries such as "tax law improvement" or "common contexts of meaning" counting as readability metrics, then the 16-metric count and the claim that F-KGL is most frequent would need revision.

Watch

Extended reading notes

Core claim

The paper's claim is descriptive: the published literature on readability measurement for legal and regulatory texts, as of February 2023, consists of 34 eligible studies using 16 different metrics. The Flesch-Kincaid Grade Level appears in the largest share of those studies, with the Flesch Reading Ease score and SMOG the next most common. 25 of the 34 studies, 73.5%, fall in the medical domain, almost all of them about informed consent forms; tax, general legal, financial, and medical-regulatory texts account for the rest. The authors further claim that this distribution shows no field-wide consensus on which metric fits legal text, and that most studies adopt a metric for convenience rather than demonstrated suitability.

Load-bearing premise

The central counts rest on the reviewers' judgment that each of the 34 included papers really applies a readability metric to legal text, and the review does not report an agreement measure for that screening judgment.

Editorial extensions

If this is right

  • If the counts are right, any attempt to standardize legal readability assessment starts from a field with no agreed metric; the most common choice is F-KGL, but most studies use different formulas and their scores are not directly comparable.
  • Because 73.5% of the evidence sits in informed consent forms, conclusions about legal readability are really conclusions about medical consent documents; other legal domains are empirically open territory.
  • Researchers applying NLP or machine learning to legal text have no common readability baseline to compare models or corpora, so progress on legal-text simplification will be hard to measure against a shared scale.
  • The prevalence of education-oriented formulas such as F-KGL and SMOG implies that domain-specific legal vocabulary is being scored by proxies like syllable count, which the paper argues misses semantics, repetition, and document structure.

Reading between the lines

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

  • The concentration in consent-form studies is likely a response to ethical-review pressure to prove participants understand what they sign; extending the same measurement habit to financial or tax disclosure could quickly broaden the evidence base.
  • A directly testable extension is to apply the same battery of 16 metrics to matched corpora of statutes, regulations, and consent forms; if the paper's domain imbalance is meaningful, metric agreement and grade-level outcomes should diverge across those text types.
  • The paper's discussion of Dale-Chall-style word lists suggests a concrete next step: build a legal-domain word list and compare it head-to-head against SMOG and F-KGL on the same documents.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. This systematic literature review identifies and characterizes studies that apply readability or linguistic complexity metrics to legal and regulatory texts. Following a PRISMA-style search of Scopus, Web of Science, and IEEE Xplore, the authors screened 3,566 records and retained 34 studies. The paper's central descriptive claims are that sixteen different metrics appear in this literature, that the Flesch-Kincaid Grade Level is the most frequently used metric, and that 73.5% of studies concern informed consent forms. The review also reports the domain distribution, describes the most common metrics, and argues that there is no consensus on a standard readability metric for legal texts. The paper includes a quality assessment based on a modified SURE checklist and a discussion of limitations and future directions.

Significance. If the descriptive claims can be verified, the review fills a genuine gap by mapping which readability metrics are actually used for legal and regulatory documents and by showing that the field is concentrated on informed consent forms. The PRISMA-structured search, explicit inclusion criteria, dual-reviewer screening, and third-reviewer conflict resolution are appropriate methodological elements for a systematic review. The paper also usefully distinguishes F-KGL from FRES and discusses domain-specific limitations of common formulas. However, the paper's value as a reference work depends on the correctness and internal consistency of its tables and counts; at present, several load-bearing inconsistencies make the headline statistics unverifiable. The review is therefore potentially useful but needs substantial correction before it can be relied upon.

major comments (5)
  1. [§3, Table 2] The inclusion criteria in §2 require studies to 'contain readability or linguistics measurements or methodology,' but Table 2 includes rows whose reported methods are not readability metrics. Row 5 ('Tax law improvement', method 'Consideration of rules in general terms, covering predictability, proportionality, consistency, compliance, administration, coordination and expression, etc.') and row 34 ('Common contexts of meaning', method 'Multilingual meaning problem') appear to be discussions of legal complexity or interpretation, not applications of a readability or linguistic measurement. Because the headline statistics ('sixteen different metrics', F-KGL as most frequent, 73.5% ICF) are frequencies over the included set, the inclusion of these studies can change the counts. Please re-apply the stated criteria to each row and either exclude these studies or explicitly justify their inclusion.
  2. [Table 2 vs. Table 3] The claim of 'sixteen different metrics' is not reconcilable with the extraction tables. Table 2 row 6 lists RCE as a method and the Abbreviations section defines RCE, yet Table 3 omits RCE entirely. Table 3 also omits methods listed in Table 2, including 'Certain key word number percentage' (row 2), 'Cetinkaya and Uzun's formula' (row 4), 'Ateşman' (rows 4 and 22), and 'FRE' as listed in row 19. Conversely, Table 3 includes 'Grammatical intricacy and lexical density' and 'Cloze Procedure' as metric categories. Please provide a single extraction table that maps every included study to a well-defined metric taxonomy and derive the 'sixteen metrics' count from that table.
  3. [Table 3, F-KGL row] The F-KGL frequency data are internally inconsistent. Table 3's F-KGL row cites [31], but Table 2 row 4 (reference [31]) reports Cloze Procedure, Cetinkaya and Uzun's formula, FRES, and Ateşman, not F-KGL. The same row omits [34] (Table 2 row 7), [9] (Table 2 row 28), and [46] (Table 2 row 22), all of which list F-KGL in Table 2. Because the central claim that F-KGL is the most frequently used metric is a count over these citations, the F-KGL frequency must be recomputed from a corrected, internally consistent version of Table 2 and Table 3.
  4. [Table 4] The domain counts in Table 4 do not sum to the 34 included studies: the table reports 25 ICF + 4 Tax + 2 Legal + 1 Finance + 1 Medical Regulations = 33, and the percentages sum to 96.9%. Table 2 lists three studies in the legal/other legal domain (rows 20, 33, and 34), not two, and uses both 'legal' and 'Other legal' category labels. Please reconcile the domain coding, the category labels, and the totals, and recompute the percentages from the corrected counts.
  5. [§2, Quality Assessment] The quality assessment is not verifiable as presented. Section 2 states that 'The quality score of each paper is shown in Appendix 2,' but no appendix is present in the manuscript, and the statement that all included studies score full marks on the first three items is unsupported by any per-study data. In addition, while the paper reports that two independent reviewers screened and extracted data, it reports no measure of inter-rater agreement (for example, Cohen's kappa) for screening, extraction, or quality scoring. Without these materials, the reliability of the inclusion decisions and the quality claims cannot be assessed.
minor comments (5)
  1. [Figure 1] The manuscript references a PRISMA flowchart in Figure 1, but no figure content appears in the text provided; please ensure the figure is actually included in the submitted version.
  2. [Abbreviations] The Abbreviations section lists 'Informed Consent Forms = ICF' twice, and 'Flesh readability ease score' should read 'Flesch readability ease score.'
  3. [Conclusions] The Conclusions section refers to 'F-KCL' rather than F-KGL; please correct the acronym.
  4. [§1.1] There are numerous language issues, such as 'According to researcher,' 'Legal law field language, which includes complex sentences archaic or apply large amount of words,' and 'what are been deemed as simple and useful regulation is always debatable.' A careful language edit is needed.
  5. [Table 3] Table 3 contains encoding issues and inconsistent naming, including 'Ate¸sman' and 'Dale-Chale,' and it is not always clear whether 'FRE' (Table 2 row 19) and 'GFOG' (Table 2 row 13) are intended to be the same as FRES and Gunning Fog; please standardize the metric names.

Circularity Check

0 steps flagged · score 1.0 of 10

Systematic review with no derivation chain to reduce; one non-load-bearing self-citation does not make the central claims circular.

full rationale

This paper is a systematic literature review, not a derivation or model-building exercise. Its central claims are descriptive counts: sixteen readability metrics were identified, Flesch-Kincaid Grade Level was the most frequent, and 73.5% of studies concerned informed consent forms. These claims rest on the authors' screening and extraction of 34 studies, which is an empirical cataloging procedure, not a chain of reasoning whose output is equivalent to its input. The only potential circularity concern is that included study [9] is by a co-author (Bergmann), and the paper also cites other work by the same group ([22], [72]); however, the headline statistics do not depend on those studies, and the review compares against a broad external literature rather than relying on the authors' own prior results as evidence for the enumeration or frequencies. The internal inconsistencies flagged by the skeptic, such as Table 2 containing rows whose methods are not readability metrics and Table 3's F-KGL citation list disagreeing with Table 2, are correctness and reproducibility concerns about the review's extraction, not circularity in the sense of a prediction reducing to a fitted input or a result being forced by self-citation. No equation, metric, or parameter is fitted from the data and then renamed as a finding. Thus, the circularity burden is very low and warrants only a minimal score.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claims of this review rest on the completeness and correctness of the search and inclusion process. No free parameters are fitted, and no new entities are proposed. The most consequential assumptions are the completeness of the chosen databases and keywords, and the consistent application of inclusion criteria. The latter is questionable because several listed studies do not clearly apply readability metrics.

assumptions (4)
  • domain assumption The three databases (Scopus, Web of Science, IEEEXplore) provide sufficient coverage of the legal readability literature.
    The review limited its search to these databases and notes that different choices could yield different outcomes; no additional database or search engine was used.
  • domain assumption The keyword combination (complex* OR metric* OR measur*) AND (readability OR linguistic) AND (regulat* OR law OR legislation) captures all relevant studies.
    The search is narrow and could miss papers using terms like 'plain language' or 'comprehension' without the word 'readability'; the paper does not test alternative keyword sets.
  • domain assumption The inclusion criteria are applied consistently to all screened papers.
    Table 2 includes papers whose methods do not look like readability metrics (e.g., No. 34 with 'multilingual meaning problem'), suggesting the criteria are either too broad or applied inconsistently.
  • ad hoc to paper The modified SURE checklist is a valid quality assessment tool for this body of work.
    The authors adapted the SURE tool and used it, but they do not validate this adaptation for legal readability studies, and the quality scores are reported as full marks on the first three items for all studies without a complete appendix.

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Cite this review

Pith. "Pith review of The Use of Readability Metrics in Legal Text: A Systematic Literature Review." pith.science (2026). https://pith.science/paper/Q3ZT6MT2

@misc{pith2026241109497,
  author       = {Pith},
  title        = {Pith review of: The Use of Readability Metrics in Legal Text: A Systematic Literature Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q3ZT6MT2}},
  note         = {Machine review of arXiv:2411.09497}
}
read the original abstract

Understanding the text in legal documents can be challenging due to their complex structure and the inclusion of domain-specific jargon. Laws and regulations are often crafted in such a manner that engagement with them requires formal training, potentially leading to vastly different interpretations of the same texts. Linguistic complexity is an important contributor to the difficulties experienced by readers. Simplifying texts could enhance comprehension across a broader audience, not just among trained professionals. Various metrics have been developed to measure document readability. Therefore, we adopted a systematic review approach to examine the linguistic and readability metrics currently employed for legal and regulatory texts. A total of 3566 initial papers were screened, with 34 relevant studies found and further assessed. Our primary objective was to identify which current metrics were applied for evaluating readability within the legal field. Sixteen different metrics were identified, with the Flesch-Kincaid Grade Level being the most frequently used method. The majority of studies (73.5%) were found in the domain of "informed consent forms". From the analysis, it is clear that not all legal domains are well represented in terms of readability metrics and that there is a further need to develop more consensus on which metrics should be applied for legal documents.

Figures

Figures reproduced from arXiv: 2411.09497 by the authors.

Figure 1
Figure 1. PRISMA flowchart of systematic review. The process consists of identification, dupli [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluating the Evaluators: Are readability metrics good measures of readability?

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    Classic readability formulas correlate weakly with human readability judgments for plain-language summaries (FKGL r=0.16), the best LLM judge reaches r=0.56, and the two evaluator families rank datasets nearly opposite.

  2. Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliance

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    A retrieval-augmented system classifies applicability of Chinese and US medical device standards from free-text device descriptions, reporting 73% accuracy and 87% top-5 recall on a 105-item benchmark.

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Pith tools

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