TextEconomizer: New Framework Achieves Efficient Lossy Text Compression with Transformer Models
Researchers have published TextEconomizer, an encoder-decoder framework using denoising transformers and entropy coding to compress text by 50–80% while preserving semantic meaning. The system operates with approximately 153 times fewer parameters than comparable models and achieves a 5.39x compression ratio without significant loss in text quality. The work addresses a gap in integrating context vectors and entropy coding into sequence-to-sequence generation, with potential applications in summarization, automated analysis, and digital archiving.
Published in Neural Networks (Elsevier, Vol. 203, 2026), TextEconomizer introduces an encoder-decoder architecture paired with a transformer neural network capable of reducing variable-sized text inputs by 50% to 80% without requiring prior knowledge of dataset dimensions. The framework incorporates entropy coding to enhance storage efficiency and is evaluated using BLEU, ROUGE, METEOR, and semantic similarity metrics, achieving near-perfect text quality scores. Notably, the system uses roughly 153 times fewer parameters than comparable transformer-based models while maintaining a 5.39x compression ratio. The paper also evaluates two additional architectures: an LSTM-based autoencoder reaching a state-of-the-art 67x compression ratio with 196 times fewer parameters, and LLaMAFormer, a modified transformer with 263 times fewer parameters than the ICAE baseline. The authors argue that TextEconomizer represents a meaningful advance in balancing memory efficiency with high-fidelity output in the lossy text compression domain.
What's missing
The paper does not appear to detail performance on out-of-domain or multilingual text, leaving open questions about generalizability beyond the evaluated datasets. The trade-offs between the 50–80% compression range and corresponding quality degradation at different compression levels are not elaborated in the abstract. Long-term practical deployment considerations, such as decompression speed and computational cost at inference time, are also not addressed.
What different sources said
- arXiv cs.CLCenter
TextEconomizer: Enhancing Lossy Text Compression with Denoising Transformers and Entropy Coding
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