An Enhanced Text Compression Approach Using Transformer-based Language Models

• URL: http://arxiv.org/abs/2412.15250v1
• Date: Sun, 15 Dec 2024 03:01:17 GMT
• Title: An Enhanced Text Compression Approach Using Transformer-based Language Models
• Authors: Chowdhury Mofizur Rahman, Mahbub E Sobhani, Anika Tasnim Rodela, Swakkhar Shatabda,
• Abstract summary: We propose a transformer-based method named RejuvenateForme for text decompression. Our meticulous pre-processing technique incorporates the Le-Ziv-Welch algorithm. The RejuvenateForme achieves a BLEU score of 27.31, 25.78, and 50.45 on the EN-DE, EN-FR, and BookCorpus corpora, showcasing its comprehensive efficacy.
• Score: 1.2937020918620652
• License:
• Abstract: Text compression shrinks textual data while keeping crucial information, eradicating constraints on storage, bandwidth, and computational efficacy. The integration of lossless compression techniques with transformer-based text decompression has received negligible attention, despite the increasing volume of English text data in communication. The primary barrier in advancing text compression and restoration involves optimizing transformer-based approaches with efficient pre-processing and integrating lossless compression algorithms, that remained unresolved in the prior attempts. Here, we propose a transformer-based method named RejuvenateForme for text decompression, addressing prior issues by harnessing a new pre-processing technique and a lossless compression method. Our meticulous pre-processing technique incorporating the Lempel-Ziv-Welch algorithm achieves compression ratios of 12.57, 13.38, and 11.42 on the BookCorpus, EN-DE, and EN-FR corpora, thus showing state-of-the-art compression ratios compared to other deep learning and traditional approaches. Furthermore, the RejuvenateForme achieves a BLEU score of 27.31, 25.78, and 50.45 on the EN-DE, EN-FR, and BookCorpus corpora, showcasing its comprehensive efficacy. In contrast, the pre-trained T5-Small exhibits better performance over prior state-of-the-art models.

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