A new readability yardstick.
By R. Flesch. 1948
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Douwe Kiela
- Mohit Bansal
- Iryna Gurevych
- Eric Wallace
- Scott Andrew Crossley
- Danielle S. McNamara
- A. Graesser
- Percy Liang
Citations in the corpus
- Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “The quality of the free-text explanations can also be evaluated in terms of readability based on frequently used readability measures, such as Gunning Fog Index (Gunning [1952]), Flesch Reading Ease (Flesch [1948]), Flesch Kincaid Grade Level (Kincaid et al. [1975]), Automated Readability Index (Sen”
- Analyzing Dynamic Adversarial Training Data in the Limit (2021, methodology): “We also experimented with BERTScore (Zhang et al., 2019) and found similar trends as BLEU score. its length in words, its Flesch-Kincaid readabil- 479 ity (Flesch, 1948), and its syntactic complexity 480 using Yngve scores (Yngve, 1960; Roark et al., 481 2007).”
- SMOG Grading - A New Readability Formula. (1969, background): “Fortunately there is no need to follow Flesch's ( 3 ) system of counting every syllable in a passage in order to obtain a valid measure of its semantic difficulty.”
- Argumentation Mining in User-Generated Web Discourse (2016, methodology): “ or sentences negatively affects the agreement. r Readability measures. We tested four standard readability measures, namely Ari (Senter and Smith 1967), Coleman-Liau (Coleman and Liau 1975), Flesch (Flesch 1948), and Lix (Björnsson 1968) to find out whether readability of the documents plays any rol”
- The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter (2014, methodology): “(Table 11) We measure readability by using Flesch reading ease (Flesch, 1948) and Flesch-Kincaid grade level”
- Review of "Mining the Web: discovering knowledge from hypertext data" by Soumen Chakrabati. Morgan Kaufman 2003. (2002, unknown): “The FleschKincaid index is a hand-tuned ‘classifier’ of sorts, combining the number of syllables per word and number of words per sentence into a index of difficulty of reading technical documents [77].”
- Human-Explainable Features for Job Candidate Screening Prediction (2017, methodology): “More specifically, we used 8 measures as features for the Readability representation: ARI [22], Flesch Reading Ease [12], FleschKincaid Grade Level [15], Gunning Fog Index [13], SMOG Index [16], Coleman Liau Index [8], LIX, and RIX [2].”
- The Role of Control in Childhood Anxiety Disorders (2003, unknown): “Moreover, the “Flesch Reading Ease” score is high (i.e., 81%; see Flesch, 1948; Thomas, Hartley, & Kincaid, 1975).”
- Risk reporting by the largest UK companies: readability and lack of obfuscation (2007, methodology): “The method most frequently used to test for readability in these studies is the Flesch Reading Ease formula ( Flesch, 1948”
- How Readability Shapes Social Media Engagement (2018, background): “Word complexity is often proxied as word length or number of syllables (e.g., Flesch, 1948; Gunning, 1952; McLaughlin, 1969).”
- The Quest for Citations: Drivers of Article Impact (2006, unknown): “Expositional clarity can also be operationalized by formal indices of readability in linguistics (Flesch 1948).”
- Advancing candidate link generation for requirements tracing: the study of methods (2006, background): “5 and the Flesch-Kincaid Grade Level is 12 [15], [16].”
- Even Einstein Struggled: Effects of Learning About Great Scientists’ Struggles on High School Students’ Motivation to Learn Science (2016, methodology): “Assessments of the nine stories confirmed comparable sentence lengths, word counts, vocabulary levels, and reading ease, as measured by the Flesch (1948) Reading Ease metric, ensuring that content difficulty and overall readability was comparable across all conditions as well as compatible with…”