# Split and Rephrase: Better Evaluation and a Stronger Baseline

arXiv: https://arxiv.org/abs/1805.01035 · HTML: https://mlsanity.com/paper/1805.01035/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 15 of 15)

- [Text Generation by Learning from Demonstrations](https://mlsanity.com/paper/2009.07839/) (2020)
- [(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance](https://mlsanity.com/paper/2108.05682/) (2021)
- [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)
- [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)
- [Posthoc Verification and the Fallibility of the Ground Truth](https://mlsanity.com/paper/2106.07353/) (2021)
- [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)
- [WikiSplit++: Easy Data Refinement for Split and Rephrase](https://mlsanity.com/paper/2404.09002/) (2024)
- [A Survey of Parameters Associated with the Quality of Benchmarks in NLP](https://mlsanity.com/paper/2210.07566/) (2022)
- [Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions](https://mlsanity.com/paper/2308.00425/) (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)

## Abstract (arXiv)

Splitting and rephrasing a complex sentence into several shorter sentences that convey the same meaning is a challenging problem in NLP. We show that while vanilla seq2seq models can reach high scores on the proposed benchmark (Narayan et al., 2017), they suffer from memorization of the training set which contains more than 89% of the unique simple sentences from the validation and test sets. To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8.68 BLEU and fostering further progress on the task.