Text-Preserving Lossy Text Compression: A Study of Strategic Deletion and LLM Reconstruction
Traditional lossless text compression preserves every byte, but its gains on natural language are often modest in realistic operating regimes. We study \emph{lossy semantic text compression}, where the encoder strategically deletes parts of the text and a large language model (LLM) reconstructs the original content from the retained skeleton. We benchmark a progression of deletion strategies, including uniform step deletion, word-length-guided deletion (WordLen), word-frequency-guided deletion (WordFreq), LP-optimized deletion (Opt), entropy-based deletion using GPT-2 surprisal, and hybrid methods that combine frequency and surprisal signals. Evaluation on the BBC News dataset across rete...
| Authors | Yuchun Zou, Junhong Tong, Jun Li |
|---|---|
| Primary category | cs.CL |
| Published | 27 May 2026 |
| Last updated at source | 1 October 2026 |
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