# Split and Rephrase

arXiv: https://arxiv.org/abs/1707.06971 · HTML: https://mlsanity.com/paper/1707.06971/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2017.

## Built on by (most cited first, 24 of 24)

- [Document-Level Text Simplification: Dataset, Criteria and Baseline](https://mlsanity.com/paper/2110.05071/) (2021)
- [(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance](https://mlsanity.com/paper/2108.05682/) (2021)
- [Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks](https://mlsanity.com/paper/2212.01350/) (2022)
- [Neural Pipeline for Zero-Shot Data-to-Text Generation](https://mlsanity.com/paper/2203.16279/) (2022)
- [BiSECT: Learning to Split and Rephrase Sentences with Bitexts](https://mlsanity.com/paper/2109.05006/) (2021)
- [Exploring Decomposition for Table-based Fact Verification](https://mlsanity.com/paper/2109.11020/) (2021)
- [Nutri-bullets: Summarizing Health Studies by Composing Segments](https://mlsanity.com/paper/2103.11921/) (2021)
- [ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences](https://mlsanity.com/paper/2106.12027/) (2021)
- [Split and Rephrase with Large Language Models](https://mlsanity.com/paper/2312.11075/) (2023)
- [Automatic and Human-AI Interactive Text Generation](https://mlsanity.com/paper/2310.03878/) (2023)
- [Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM](https://mlsanity.com/paper/2407.04067/) (2024)
- [Text Simplification for Comprehension-based Question-Answering](https://mlsanity.com/paper/2109.13984/) (2021)
- [Unsupervised Simplification of Legal Texts](https://mlsanity.com/paper/2209.00557/) (2022)
- [With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms](https://mlsanity.com/paper/2101.10096/) (2021)
- [Mitigating Data Scarcity for Large Language Models](https://mlsanity.com/paper/2302.01806/) (2023)
- [WikiSplit++: Easy Data Refinement for Split and Rephrase](https://mlsanity.com/paper/2404.09002/) (2024)
- [Policy Compliance Detection via Expression Tree Inference](https://mlsanity.com/paper/2205.12259/) (2022)
- [The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting](https://mlsanity.com/paper/2302.00937/) (2023)
- [Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions](https://mlsanity.com/paper/2308.00425/) (2023)
- [Conjunct Resolution in the Face of Verbal Omissions](https://mlsanity.com/paper/2305.16740/) (2023)
- [CEScore: Simple and Efficient Confidence Estimation Model for Evaluating Split and Rephrase](https://mlsanity.com/paper/2312.01356/) (2023)
- [From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences](https://mlsanity.com/paper/2312.05172/) (2023)
- [Text Adaptation to Plain Language and Easy Read via Automatic Post-Editing Cycles](https://mlsanity.com/paper/2509.11991/) (2025)
- [Sentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning](https://mlsanity.com/paper/2607.19845/) (2026)

## Abstract (arXiv)

We propose a new sentence simplification task (Split-and-Rephrase) where the aim is to split a complex sentence into a meaning preserving sequence of shorter sentences. Like sentence simplification, splitting-and-rephrasing has the potential of benefiting both natural language processing and societal applications. Because shorter sentences are generally better processed by NLP systems, it could be used as a preprocessing step which facilitates and improves the performance of parsers, semantic role labellers and machine translation systems. It should also be of use for people with reading disabilities because it allows the conversion of longer sentences into shorter ones. This paper makes two contributions towards this new task. First, we create and make available a benchmark consisting of 1,066,115 tuples mapping a single complex sentence to a sequence of sentences expressing the same meaning. Second, we propose five models (vanilla sequence-to-sequence to semantically-motivated models) to understand the difficulty of the proposed task.