Automate Wikipedia Data Extraction & Summarization with n8n & Google Gemini

Opening Problem Statement

Meet Sarah, a market researcher tasked with compiling comprehensive yet concise reports on emerging technologies for her company. One day, she faces the daunting task of gathering up-to-date, detailed information from Wikipedia pages about cloud computing without spending hours sifting through raw web data or hiring expensive contractors. Manually copying, cleaning, and summarizing these pages is painfully slow, error-prone, and can easily derail important deadlines, costing her company time and money.

This is the precise situation this n8n workflow solves—automating the extraction and summarization of complex Wikipedia data using reliable web scraping and cutting-edge AI summarization. Instead of wasting hours, Sarah now can get rich, human-readable summaries in minutes, freeing her to focus on analysis and decision-making rather than data wrangling.

What This Automation Does

When you trigger this workflow in n8n, it takes a specific Wikipedia article URL and performs a series of automated steps to deliver a concise human-friendly summary. Here’s what it accomplishes:

• Uses Bright Data’s web unlocking zone to scrape the full HTML content of a Wikipedia article reliably, avoiding CAPTCHAs and geo-blocks.
• Converts the raw HTML data into clean, human-readable text using the LLM Data Extractor powered by Google Gemini AI.
• Generates a concise summary of the extracted content with a specialized summarization chain, again leveraging Google Gemini’s AI capabilities.
• Automatically sends the final summary to an external HTTP webhook for notifications or further processing.
• Uses manual triggering to allow on-demand runs, making it ideal for research tasks or workflows requiring precise timing.
• Supports easy customization of the Wikipedia URL and Bright Data zone credentials, providing flexibility for different scraping needs.

With this automation, users save hours of manual effort, reduce errors in data processing, and receive expertly formatted summaries with a few clicks.

Prerequisites ⚙️

• n8n account to build and execute workflows.
• Bright Data account with a configured “web_unlocker1” zone to scrape Wikipedia articles without blocks. (🔐 credentials required in n8n)
• Google Gemini (PaLM) API access for two AI-powered nodes: one for data extraction and one for summarization. (🔐 credentials required in n8n)
• Webhook endpoint URL for receiving summarized data.

Step-by-Step Guide to Build and Run This Workflow

Step 1: Setup Manual Trigger

Navigate to your n8n editor interface, click + Add Node, and select Manual Trigger. This node starts the workflow only when you click “Execute Workflow.” It’s perfect for controlled runs. You should see a button labeled “Execute Workflow” once you save this node.

Common mistake: Forgetting to trigger manually can make it seem like the workflow doesn’t run automatically.

Step 2: Set Wikipedia URL with Bright Data Zone

Add a Set node next. Configure it to assign two fields:

• url: The target Wikipedia article URL, e.g., https://en.wikipedia.org/wiki/Cloud_computing?product=unlocker&method=api
• zone: web_unlocker1 (your Bright Data scraping zone)

After wiring the manual trigger to this Set node, save it and confirm the values. The Set node prepares inputs for the next HTTP request.

Common mistake: Not including the correct Bright Data “zone” name can cause scraping failures.

Step 3: Configure Wikipedia Web Request Node for Data Scraping

Add an HTTP Request node with these settings:

• Method: POST
• URL: https://api.brightdata.com/request
• Authentication: Use HTTP Header Auth with Bright Data credentials configured in n8n.
• Body Parameters:
• zone: {{$json.zone}}
• url: {{$json.url}}
• format: raw

This node sends a request to Bright Data’s API to scrape the raw HTML content of the Wikipedia page.

Visual cue: You should see JSON response data with raw page content under data.

Common mistake: Missing or invalid credentials cause authentication errors.

Step 4: Extract Human-Readable Data Using LLM Data Extractor

Add an LLM Chain node configured as:

• Text input: {{$json.data}} (raw HTML content from Wikipedia)
• Prompt: “You are an expert Data Formatter. Make sure to format the data in a human readable manner. Please output the human readable content without your own thoughts.”
• Use Google Gemini (PaLM API) credentials.

This node cleans and translates messy HTML into plain text that humans can easily understand.

Common mistake: Not setting the correct input text property can output empty or irrelevant results.

Step 5: Generate Concise Summary with Summarization Chain

Next, add a Chain Summarization node configured as follows:

• Summarization prompt: Write a concise summary of the following:nn"{text}"
• Chunking mode: Advanced to handle larger texts properly.
• Use Google Gemini (PaLM API) credentials.

This node receives the cleaned text and produces a brief, accurate summary.

Common mistake: Using incorrect prompt formatting can lead to vague summaries.

Step 6: Send Summary to External Webhook

Add an HTTP Request node with the following details:

• Method: POST
• URL: Your chosen webhook URL, e.g., https://webhook.site/ce41e056-c097-48c8-a096-9b876d3abbf7
• Send body: Yes
• Body parameter:
  • Name: summary
  • Value: {{$json.response.text}} (summary text from previous node)

This step ensures the summarized information is pushed to your desired system or alert channel for further use.

Common mistake: Forgetting to enable “Send Body” causes empty webhook payloads.

Step 7: Connect and Activate Workflow

Wire the nodes in this order:

• Manual Trigger → Set Wikipedia URL
• Set Wikipedia URL → Wikipedia Web Request
• Wikipedia Web Request → LLM Data Extractor
• LLM Data Extractor → Concise Summary Generator
• Concise Summary Generator → Summary Webhook Notifier

Save and activate your workflow. Click on the “Execute Workflow” button to run the process manually. You should see data flowing through each node and a final webhook response containing your concise summary.

Customizations ✏️

• Change Wikipedia Article: In the Set Wikipedia URL with Bright Data Zone node, update the url field to any other Wikipedia page you want to scrape and summarize.
• Use Alternative AI Models: Replace the Google Gemini nodes with OpenAI or any other preferred LLM nodes by adjusting credentials and model names in the LLM Data Extractor and Concise Summary Generator.
• Adjust Summarization Prompt: Customize the prompt in the Concise Summary Generator node to change the tone or detail level of the summary output.
• Change Webhook Endpoint: Update the URL in the Summary Webhook Notifier node to integrate with different notification services like Slack or your own API.

Troubleshooting 🔧

• Problem: “Authentication failed” error on Wikipedia Web Request node.
  Cause: Incorrect or expired Bright Data credentials.
  Solution: Go to the HTTP Request node settings → Authentication → Re-enter valid Bright Data HTTP Header Auth credentials.
• Problem: Empty or garbled text output from LLM Data Extractor.
  Cause: Input data not correctly passed or prompt misconfigured.
  Solution: Check that the text parameter input references {{$json.data}} and prompt messages are clear, concise, and without extra instructions.
• Problem: Summary Webhook Notifier sends empty payload.
  Cause: “Send Body” option disabled or incorrect field reference.
  Solution: Enable “Send Body” and verify body parameter references {{$json.response.text}}.

Pre-Production Checklist ✅

• Verify Bright Data credentials and zone name are correctly entered in the HTTP Request node.
• Confirm Google Gemini (PaLM API) credentials are active and tested for both LLM nodes.
• Test the manual trigger and ensure data flows through each node sequentially without errors.
• Check that the final summarized output is accurate and sent to your webhook URL.
• Backup all credential info and workflow settings securely.

Deployment Guide

Activate the workflow in n8n by turning on the toggle switch. Since this workflow uses a manual trigger, run it whenever you need fresh Wikipedia summaries.

Monitor workflow executions in n8n’s workflow run log to confirm successful scraping and summarization.

If deploying in self-hosted environment, mention services like Hostinger for stable n8n hosting.

Frequently Asked Questions (FAQs)

• Can I use OpenAI instead of Google Gemini for summarization?
  Yes, by swapping the LLM nodes to use OpenAI credentials and models, the workflow remains functional.
• Will this workflow handle long Wikipedia articles?
  Yes, the summarization node uses advanced chunking to process large text inputs effectively.
• Is my scraped data secure?
  All credentials and data flow within your n8n instance. Use encrypted credentials and secure your webhook endpoints for best practices.
• Does this consume API credits?
  Yes, Bright Data and Google Gemini APIs have usage limits and costs depending on your plan.

Conclusion

By completing this tutorial, you’ve built a powerful automation that transforms raw Wikipedia pages into neat, human-readable summaries using Bright Data’s scraping and Google Gemini’s AI. This process saves countless hours of manual research and data cleaning, giving you crisp insights in minutes.

Next, you might explore integrating this summary output with Slack or email to notify teams automatically or extend the workflow to scrape and summarize multiple pages in bulk.

Embrace automation like this to stay informed faster, work smarter, and make decisions with confidence. Happy automating!