LLM Context Bloat got you down? Learn Text Summarization in this Tutorial

In this video, you'll learn 3 different strategies (with code) for text summarization. You can use this in local AI setups to summarize longer articles, deal with long LLM responses or in hybrid setups to reduce token spend and context bloat.

You can follow along with the notebook here: https://github.com/kjam/personalized-ai/blob/main/getting-started/Context%20Bloat%20Strategies%20(aka%20Summarization!).ipynb

00:00 Why Summarize Text
01:27 Setup and Notebook Tour
01:51 TextRank Overview
03:24 Implementing PyTextRank
05:09 Tuning Cutoffs and Phrases
07:41 Ranking Sentences Summary
10:16 When to Use TextRank
12:17 Summarizing with Local LLM
13:13 Limits of LLM Summaries
14:28 Task Specific Models
15:43 Chunking with Pegasus XSum
17:54 GPU Demo Results
20:01 Wrap Up and Questions

Additional Links:
- Notebook: https://github.com/kjam/personalized-ai/blob/main/getting-started/Context%20Bloat%20Strategies%20(aka%20Summarization!).ipynb
- SpaCy: https://spacy.io/
- pytextrank: https://derwen.ai/docs/ptr/explain_algo/
- Google's Pegasus Xsum: https://huggingface.co/google/pegasus-xsum

What are you using summarization for? Did you try out any of my examples? I'd love to hear from you in the comments. :)

Видео LLM Context Bloat got you down? Learn Text Summarization in this Tutorial канала Probably Private