Parameters such as temperature, prompt size, token limit, top_p, and goal task are just a few of the things to manage with a GPT-3 text summarizer. With GPT-3 specifically, you have a number of different variables to take into account that make it different from other summarization architectures. The type of architecture you need to go from experimenting in summarization to a production system that supports a huge range of text sizes and document types is entirely different. What happens when you want to have a bit more input to what you consider a “good” summarization or go from 1 paragraph to 8? As the data variance changes and you look to have more input to what your summarization looks like the difficulty grows rapidly. I’m sure you’ve seen it’s not incredibly difficult in a playground environment with simple tasks such as paragraphs and a small dataset. Models such as GPT-3 have made it easy for anyone to get started in text summarization to some level.Īs we continue to push the bounds of text summarization it’s easy to see why it’s considered one of the most challenging fields to perfect. These advances in transformers and large language models have driven the game changing summarization abilities seen right now.
We’ve moved from being able to summarize paragraphs and short pages to being able to use large language models to summarize entire books (thanks to OpenAi) or long research papers. The field of text summarization for input texts of all different types and sizes continues to grow in 2022, especially as deep learning continues to push forward and expand the range of use cases possible. The key components to building a gpt-3 summarizer with short & long-form summarization for news articles, blog posts, legal documents, and more.
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