# Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification

arXiv: https://arxiv.org/abs/2010.10035 · HTML: https://mlsanity.com/paper/2010.10035/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 27 of 27)

- [Paragraph-level Simplification of Medical Texts](https://mlsanity.com/paper/2104.05767/) (2021)
- [Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling](https://mlsanity.com/paper/2402.17019/) (2024)
- [Retrieval augmentation of large language models for lay language generation](https://mlsanity.com/paper/2211.03818/) (2022)
- [FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence](https://mlsanity.com/paper/2402.11456/) (2024)
- [Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts](https://mlsanity.com/paper/2302.09155/) (2023)
- [APPLS: Evaluating Evaluation Metrics for Plain Language Summarization](https://mlsanity.com/paper/2305.14341/) (2023)
- [Elaborative Simplification as Implicit Questions Under Discussion](https://mlsanity.com/paper/2305.10387/) (2023)
- [Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion](https://mlsanity.com/paper/2210.05905/) (2022)
- [Multilingual Simplification of Medical Texts](https://mlsanity.com/paper/2305.12532/) (2023)
- [SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages](https://mlsanity.com/paper/2305.19204/) (2023)
- [Personalized Jargon Identification for Enhanced Interdisciplinary Communication](https://mlsanity.com/paper/2311.09481/) (2023)
- [Conversational User-AI Intervention: A Study on Prompt Rewriting for Improved LLM Response Generation](https://mlsanity.com/paper/2503.16789/) (2025)
- [Enhancing Biomedical Lay Summarisation with External Knowledge Graphs](https://mlsanity.com/paper/2310.15702/) (2023)
- [Bridging Background Knowledge Gaps in Translation with Automatic Explicitation](https://mlsanity.com/paper/2312.01308/) (2023)
- [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025)
- [Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation](https://mlsanity.com/paper/2404.03278/) (2024)
- [Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts](https://mlsanity.com/paper/2410.20763/) (2024)
- [Free-text Rationale Generation under Readability Level Control](https://mlsanity.com/paper/2407.01384/) (2024)
- [PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization](https://mlsanity.com/paper/2503.08890/) (2025)
- [Explanatory Summarization with Discourse-Driven Planning](https://mlsanity.com/paper/2504.19339/) (2025)
- [Context-Aware Document Simplification](https://mlsanity.com/paper/2305.06274/) (2023)
- [Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding](https://mlsanity.com/paper/2310.11191/) (2023)
- [REFeREE: A REference-FREE Model-Based Metric for Text Simplification](https://mlsanity.com/paper/2403.17640/) (2024)
- [Cognitive Simplification Operations Improve Text Simplification](https://mlsanity.com/paper/2211.08825/) (2022)
- [Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation](https://mlsanity.com/paper/2505.10409/) (2025)
- [JEBS: A Fine-grained Biomedical Lexical Simplification Task](https://mlsanity.com/paper/2506.12898/) (2025)
- [Automatic Simplification of Common Vulnerabilities and Exposures Descriptions](https://mlsanity.com/paper/2602.11982/) (2026)

## Abstract (arXiv)

Much of modern-day text simplification research focuses on sentence-level simplification, transforming original, more complex sentences into simplified versions. However, adding content can often be useful when difficult concepts and reasoning need to be explained. In this work, we present the first data-driven study of content addition in text simplification, which we call elaborative simplification. We introduce a new annotated dataset of 1.3K instances of elaborative simplification in the Newsela corpus, and analyze how entities, ideas, and concepts are elaborated through the lens of contextual specificity. We establish baselines for elaboration generation using large-scale pre-trained language models, and demonstrate that considering contextual specificity during generation can improve performance. Our results illustrate the complexities of elaborative simplification, suggesting many interesting directions for future work.