mlsanityData to 7 Oct 2026

Text Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus

arXiv 2209.04529 ↗First posted 9 Sep 2022cs.CLcited by 1

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Abstract (arXiv)

Access to higher education is critical for minority populations and emergent bilingual students. However, the language used by higher education institutions to communicate with prospective students is often too complex; concretely, many institutions in the US publish admissions application instructions far above the average reading level of a typical high school graduate, often near the 13th or 14th grade level. This leads to an unnecessary barrier between students and access to higher education. This work aims to tackle this challenge via text simplification. We present PSAT (Professionally Simplified Admissions Texts), a dataset with 112 admissions instructions randomly selected from higher education institutions across the US. These texts are then professionally simplified, and verified and accepted by subject-matter experts who are full-time employees in admissions offices at various institutions. Additionally, PSAT comes with manual alignments of 1,883 original-simplified sentence pairs. The result is a first-of-its-kind corpus for the evaluation and fine-tuning of text simplification systems in a high-stakes genre distinct from existing simplification resources.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 68 references that are in the map. First posted on arXiv 9 Sep 2022.

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12 of 68 references.

References · 12

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  3. 2017Sentence Simplification with Deep Reinforcement Learningcited by 107
  4. 2020Neural CRF Model for Sentence Alignment in Text Simplificationcited by 106
  5. 2019Controllable Sentence Simplificationcited by 78
  6. 2019EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editingcited by 63
  7. 2020Controllable Text Simplification with Explicit Paraphrasingcited by 53
  8. 2021Paragraph-level Simplification of Medical Textscited by 52
  9. 2021AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarizationcited by 51
  10. 2021ASPECTNEWS: Aspect-Oriented Summarization of News Documentscited by 37
  11. 2022Evaluating Factuality in Text Simplificationcited by 34
  12. 2019Complexity-Weighted Loss and Diverse Reranking for Sentence Simplificationcited by 20

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