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Topic · Artificial Intelligence

Text Readability and Simplification

Text Readability and Simplification is a research topic within Artificial Intelligence. Science Explorer counts 11k research works in it since 1950. 23.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on automatic text simplification and readability assessment using machine learning, statistical language models, neural networks, and natural language processing techniques. The research covers areas such as sentence simplification, lexical simplification, complex word identification, and semantic simplification to improve the accessibility and comprehension of written text.

  • Text Simplification
  • Readability Assessment
  • Machine Learning
  • Sentence Simplification
  • Statistical Language Models
  • Neural Networks
  • Lexical Simplification
  • Natural Language Processing
  • Complex Word Identification
  • Semantic Simplification
Research works
11k
fractional, since 1950
In the world top 10%
2.5k
per year above
Top-10% rate
23.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+53%
the tick is no change

Which countries lead Text Readability and Simplification research?

By volume, the United States and China publish the most (514 and 373 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 514 works
  2. 2 China 373 works
  3. 3 India 159 works
  4. 4 Germany 135 works
  5. 5 United Kingdom 119 works
  6. 6 Japan 110 works
  7. 7 Spain 100 works
  8. 8 Türkiye 99 works
  9. 9 France 94 works
  10. 10 Italy 82 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 28.8%China: 20.9%India: 8.9%Germany: 7.5%6 others listed: 33.9%29%largest
United States514 · 28.8%China373 · 20.9%India159 · 8.9%Germany135 · 7.5%6 others listed605 · 33.9%

Shares of the rows listed above, not of the whole node.

Which institutions lead Text Readability and Simplification research?

By volume in 2022–2025, Peking University publishes the most Text Readability and Simplification research, followed by King Saud University and New York University.

By volume, 2022–2025

  1. 1 Peking UniversityChina 12 works
  2. 2 King Saud UniversitySaudi Arabia 11 works
  3. 3 New York UniversityUnited States 10 works
  4. 4 Hong Kong Polytechnic UniversityHong Kong 10 works
  5. 5 University of AmsterdamNetherlands 10 works
  6. 6 University of HelsinkiFinland 9 works
  7. 7 University of CambridgeUnited Kingdom 9 works
  8. 8 Waseda UniversityJapan 8 works
  9. 9 Carnegie Mellon UniversityUnited States 8 works
  10. 10 University of the Basque CountrySpain 8 works

Who are the leading researchers in Text Readability and Simplification?

The most-cited researchers publishing on Text Readability and Simplification include Wei Liu, Christopher D. Manning and Yu Qiao.

  1. 1 Wei Liu Australia 9.7k citations
  2. 2 Christopher D. Manning United States 4.6k citations
  3. 3 Yu Qiao China 4.3k citations
  4. 4 Dong Yu United States 4.1k citations
  5. 5 Zhiyuan Liu China 3.7k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Text Readability and Simplification research done?

The largest centres of Text Readability and Simplification research in 2022–2025 are Beijing (China), Tokyo (Japan), Shanghai (China) and New York (United States).

Largest cities, 2022–2025

  1. 1 Beijing China 94 works
  2. 2 Tokyo Japan 51 works
  3. 3 Shanghai China 35 works
  4. 4 New York United States 31 works
  5. 5 Guangzhou China 27 works
  6. 6 Hong Kong China 26 works
  7. 7 Paris France 26 works
  8. 8 Moscow Russia 25 works
  9. 9 Riyadh Saudi Arabia 24 works
  10. 10 Madrid Spain 22 works
See Text Readability and Simplification on the map

Where is the best place to study Text Readability and Simplification?

Among universities, judged by research, Hong Kong Polytechnic University, New York University and Carnegie Mellon University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 23.51%fractional works in this node (log) →share in the world top 10% →Hong Kong Polytechnic University: 10, 43.5%New York University: 10, 24.3%Carnegie Mellon University: 8, 37.2%King Saud University: 11, 23.7%Waseda University: 8, 21.3%Peking University: 12, 18.5%University of Amsterdam: 10, 15.6%University of Cambridge: 9, 30.6%University of Helsinki: 10, 6.1%University of the Basque Country: 8, 14.3%Hong Kong Polytechni…Carnegie Mellon Univ…New York UniversityKing Saud University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Hong Kong Polytechnic UniversityHong Kong 62.643.5%3.9×10 —
2 New York UniversityUnited States 49.724.3%4.4×10 +215.7%
3 Carnegie Mellon UniversityUnited States 49.437.2%6.9×8 +25.1%
4 King Saud UniversitySaudi Arabia 46.223.7%4.0×11 +19.0%
5 Waseda UniversityJapan 44.521.3%8.4×8 —
6 Peking UniversityChina 41.518.5%2.6×12 +132.4%
7 University of AmsterdamNetherlands 39.515.6%6.3×10 +2.2%
8 University of CambridgeUnited Kingdom 38.830.6%2.9×9 +60.5%
9 University of HelsinkiFinland 35.46.1%5.6×10 +139.6%
10 University of the Basque CountrySpain 23.214.3%6.3×8 -45.0%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Text Readability and Simplification research growing?

Output in 2018–2022 was 53% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Text Readability and Simplification.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.