# Syntactic Complexity Identification, Measurement, and Reduction Through Controlled Syntactic Simplification

arXiv: https://arxiv.org/abs/2304.07774 · HTML: https://mlsanity.com/paper/2304.07774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 16 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [A Survey on Open Information Extraction](https://mlsanity.com/paper/1806.05599/) (2018)
- [How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks](https://mlsanity.com/paper/2109.04604/) (2021)
- [Small but Mighty: New Benchmarks for Split and Rephrase](https://mlsanity.com/paper/2009.08560/) (2020)

## Built on by (most cited first, 1 of 1)

- [LongWanjuan: Towards Systematic Measurement for Long Text Quality](https://mlsanity.com/paper/2402.13583/) (2024)

## Abstract (arXiv)

Text simplification is one of the domains in Natural Language Processing (NLP) that offers an opportunity to understand the text in a simplified manner for exploration. However, it is always hard to understand and retrieve knowledge from unstructured text, which is usually in the form of compound and complex sentences. There are state-of-the-art neural network-based methods to simplify the sentences for improved readability while replacing words with plain English substitutes and summarising the sentences and paragraphs. In the Knowledge Graph (KG) creation process from unstructured text, summarising long sentences and substituting words is undesirable since this may lead to information loss. However, KG creation from text requires the extraction of all possible facts (triples) with the same mentions as in the text. In this work, we propose a controlled simplification based on the factual information in a sentence, i.e., triple. We present a classical syntactic dependency-based approach to split and rephrase a compound and complex sentence into a set of simplified sentences. This simplification process will retain the original wording with a simple structure of possible domain facts in each sentence, i.e., triples. The paper also introduces an algorithm to identify and measure a sentence's syntactic complexity (SC), followed by reduction through a controlled syntactic simplification process. Last, an experiment for a dataset re-annotation is also conducted through GPT3; we aim to publish this refined corpus as a resource. This work is accepted and presented in International workshop on Learning with Knowledge Graphs (IWLKG) at WSDM-2023 Conference. The code and data is available at www.github.com/sallmanm/SynSim.