CompressedBART: Fine-Tuning for Summarization through Latent Space Compression (Paper Review/Described)
Last Updated on April 11, 2023 by Editorial Team
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.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
