CompressedBART: Fine-Tuning for Summarization through Latent Space Compression (Paper Review/Described)

Author(s): Ala Alam Falaki

Originally published on Towards AI.

Paper title: A Robust Approach to Fine-tune Pre-trained Transformer-based Models for Text Summarization through Latent Space Compression.

“Can we compress a pre-trained encoder while keeping its language generation abilities?”This is the main question that this paper is trying to answer. It solely focuses on an encoder-decoder architecture to fine-tune a text summarization model. The exciting takeaway after reading the paper could be whether the encoders generate redundant information in their representations or not. Let’s see if we can find the answer…

I feel like a broken record at this point. As I mentioned this issue multiple times in my medium, Transformer-based models… Read the full blog for free on Medium.

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Published via Towards AI