TextEconomizer: New Framework Achieves Efficient Lossy Text Compression with Transformer Models
Researchers introduced TextEconomizer, a transformer-based encoder-decoder framework that compresses text by 50-80% while preserving semantic meaning. The model uses entropy coding and context vectors to achieve compression ratios up to 5.39x with significantly fewer parameters than comparable systems. This advancement could improve efficiency in text summarization, automated analysis, and digital archiving applications.
TextEconomizer is a new lossy text compression framework that combines transformer neural networks with entropy coding to reduce file sizes while maintaining text quality. The model achieves 50-80% compression with approximately 153x fewer parameters than existing transformer-based approaches, reaching a 5.39x compression ratio while maintaining near-perfect text quality as measured by BLEU, ROUGE, METEOR, and semantic similarity metrics. The researchers also evaluated alternative architectures, including an LSTM-based autoencoder achieving 67x compression with 196x fewer parameters and a modified transformer variant (LLaMAFormer) with 263x fewer parameters. The framework addresses a gap in integrating context vectors and entropy coding into Sequence-to-Sequence generation, which had remained underexplored despite transformer dominance in language modeling. The approach is designed to work without prior knowledge of dataset dimensions, making it adaptable across different text sources.
What's missing
The paper does not discuss computational inference time or real-world deployment considerations. Additionally, the specific datasets used for evaluation and how results compare to non-neural baseline compression methods (e.g., gzip, bzip2) are not detailed in the abstract. The paper also does not address potential limitations of lossy compression for applications requiring perfect fidelity or discuss failure cases.
What different sources said
- arXiv cs.CLCenter
TextEconomizer: Enhancing Lossy Text Compression with Denoising Transformers and Entropy Coding
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