In this comprehensive how-to guide, we will walk you through the process of building an article summarization system using GPT-3, Airtable, and a Web Clipper. This system will enable you to extract the title and body of an article with just one click, store it in Airtable, and have GPT-3 generate a summarized version.
Add the Airtable Web Clipper Chrome extension: Install the Airtable Web Clipper extension to your browser to easily extract article titles and bodies.
Create an Airtable base and table: Set up a new base and table in Airtable to store your article data, with fields for the title, body, and summary.
Configure the Web Clipper: Link the Web Clipper to your Airtable base and map the fields accordingly.
Set up a webhook in Integromat: Create a new scenario in Integromat and add a webhook module to trigger instant actions.
Link the webhook to Airtable: Use custom code provided by Connor Finleyson (linked in the video description) to connect your webhook to your Airtable base.
Add an Airtable module in Integromat: Configure the module to retrieve a record by its record ID.
Add an OpenAI GPT-3 module in Integromat: Set up the module to generate a summary of the article body.
Update the Airtable summary field: Add another Airtable module in Integromat to update the summary field with the generated summary from GPT-3.
Test your workflow: Clip an article using the Web Clipper, change the status to "summarize" in Airtable, and watch as GPT-3 summarizes the article and sends the summary back to Airtable.
With this guide, you'll be able to build a seamless article summarization system, leveraging the power of GPT-3 and the convenience of Airtable.