# Syntactically Informed Text Compression with Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1608.02893 · HTML: https://mlsanity.com/paper/1608.02893/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2016.

## Built on by (most cited first, 6 of 6)

- [Language Modeling Is Compression](https://mlsanity.com/paper/2309.10668/) (2023)
- [Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data](https://mlsanity.com/paper/2410.05078/) (2024)
- [Partition and Code: learning how to compress graphs](https://mlsanity.com/paper/2107.01952/) (2021)
- [Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction](https://mlsanity.com/paper/2505.06297/) (2025)
- [Test-Time Steering for Lossless Text Compression via Weighted Product of Experts](https://mlsanity.com/paper/2511.10660/) (2025)
- [A Model-Driven Lossless Compression Algorithm Resistant to Mismatch](https://mlsanity.com/paper/2601.17684/) (2026)

## Abstract (arXiv)

We present a self-contained system for constructing natural language models for use in text compression. Our system improves upon previous neural network based models by utilizing recent advances in syntactic parsing -- Google's SyntaxNet -- to augment character-level recurrent neural networks. RNNs have proven exceptional in modeling sequence data such as text, as their architecture allows for modeling of long-term contextual information.