Automated Wikipedia Data Extraction and Summarization Workflow

This n8n workflow automates the process of extracting, processing, and summarizing Wikipedia data using Bright Data and Google Gemini AI models. It begins with a manual trigger allowing user-initiated execution, then sets a specific Wikipedia URL and zone for data scraping through Bright Data. The workflow sends a request to Bright Data’s API to fetch raw HTML data, which is then processed using a large language model (LLM) chain to extract human-readable content. This processed data is further summarized by a specialized summarization chain, providing a concise overview of the Wikipedia article. The summarized result is optionally sent to an external webhook for notification or further processing. Practical use cases include content research, data analysis, or generating quick summaries for educational or professional purposes. The workflow is flexible, with sticky notes guiding customization, such as changing the URL, using different LLM providers, or adjusting the summarization parameters.