The paper titled “A Robust Approach to Fine-tune Pre-trained Transformer-based Models for Text Summarization through Latent Space Compression” explores the ability to compress a pre-trained encoder while maintaining its language generation abilities. The study focuses on an encoder-decoder architecture for text summarization and investigates if the encoders generate redundant information in their representations. The full article can be read on Medium via “Towards AI” publication.

source update: Fine-Tuning for Summarization through Latent Space… – Towards AI


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