The paper explores a more comprehensive approach to assessing text-level difficulty by combining quantitative readability metrics with qualitative analyses of content and context which help in reading comprehension and reading-for-translation. It compares two excerpts using eight readability scores formulas (Automated Readability Index, Flesch Reading Ease, Gunning Fog Index, Flesch-Kincaid Grade Level, Coleman-Liau Readability Index, Smog Index, Original Linsear Write Formula, Linsear Write Grade Level Formula) to explore how topic, content, and context may be used as indicators of text-level difficulty. Using authentic texts, specifically interviews from Humans of New York, the paper aims to demonstrate that other (extra)linguistic features must be considered beyond the numerical scores provided by readability formulas.
Nejla Kalajdžisalihović (Thu,) studied this question.