# Transforming Complex Sentences into a Semantic Hierarchy

arXiv: https://arxiv.org/abs/1906.01038 · HTML: https://mlsanity.com/paper/1906.01038/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

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

- [BiSECT: Learning to Split and Rephrase Sentences with Bitexts](https://mlsanity.com/paper/2109.05006/) (2021)
- [Text Revision by On-the-Fly Representation Optimization](https://mlsanity.com/paper/2204.07359/) (2022)
- [Split and Rephrase with Large Language Models](https://mlsanity.com/paper/2312.11075/) (2023)
- [Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation](https://mlsanity.com/paper/2404.03278/) (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)
- [Unsupervised Sentence Simplification via Dependency Parsing](https://mlsanity.com/paper/2206.12261/) (2022)
- [Machine Translation Testing via Syntactic Tree Pruning](https://mlsanity.com/paper/2401.00751/) (2024)
- [WikiSplit++: Easy Data Refinement for Split and Rephrase](https://mlsanity.com/paper/2404.09002/) (2024)
- [Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions](https://mlsanity.com/paper/2308.00425/) (2023)
- [Edit-Constrained Decoding for Sentence Simplification](https://mlsanity.com/paper/2409.19247/) (2024)
- [Context-Preserving Text Simplification](https://mlsanity.com/paper/2105.11178/) (2021)
- [CEScore: Simple and Efficient Confidence Estimation Model for Evaluating Split and Rephrase](https://mlsanity.com/paper/2312.01356/) (2023)
- [Text Adaptation to Plain Language and Easy Read via Automatic Post-Editing Cycles](https://mlsanity.com/paper/2509.11991/) (2025)
- [SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction](https://mlsanity.com/paper/2601.10003/) (2026)

## Abstract (arXiv)

We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online.