Summary of An Enhanced Text Compression Approach Using Transformer-based Language Models, by Chowdhury Mofizur Rahman and Mahbub E Sobhani and Anika Tasnim Rodela and Swakkhar Shatabda
An Enhanced Text Compression Approach Using Transformer-based Language Models
by Chowdhury Mofizur Rahman, Mahbub E Sobhani, Anika Tasnim Rodela, Swakkhar Shatabda
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed RejuvenateForme method combines transformer-based text decompression with lossless compression techniques to achieve significant improvements in text compression ratios and quality. The paper addresses the challenge of optimizing transformer-based approaches for efficient pre-processing and integrates lossless compression algorithms. The results show state-of-the-art compression ratios on various corpora, including BookCorpus, EN-DE, and EN-FR, outperforming traditional and deep learning approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text compression is important for reducing storage needs and improving communication efficiency. This paper proposes a new method called RejuvenateForme to decompress text using transformers and lossless compression. The method has three main parts: pre-processing, compression, and decompression. The pre-processing step uses an algorithm called Lempel-Ziv-Welch to prepare the text for compression. The results show that this method can compress text very effectively, achieving ratios of 12.57, 13.38, and 11.42 on different corpora. |
Keywords
* Artificial intelligence * Deep learning * Transformer
