Article Content Extractor

Article Content Extractor is a BrowserAct Bot template for collecting structured article data from public article URLs. Provide one or more article links, set a count limit, and run the Bot to return clean article records for research, content workflows, knowledge bases, or downstream automation.

What Does BrowserAct Article Content Extractor Do?

This template opens each public article URL in order, reads standard metadata and visible article content, extracts a clean body text field, and returns one structured record per article. It is designed for non-login use cases and only collects publicly visible content.

Key Features

Process one or more public article URLs.
Preserve input order and stop at the configured count.
Extract article title, author, dates, publisher, body text, summary, hero image URL, tags, canonical URL, and source URL.
Use JSON-LD, Open Graph, canonical links, standard meta tags, and visible page fallbacks.
Return optional fields as empty when a page does not expose them.

What Data Can I Extract from Articles?

Field	Description
`title`	Article title.
`author`	Author name when available.
`published_date`	Published date from page metadata or visible content.
`updated_date`	Updated or modified date when shown.
`publisher`	Publisher or site name.
`article_text`	Clean article body text.
`summary`	Meta description or visible summary when available.
`hero_image_url`	Main article image URL when available.
`tags`	Tags, keywords, or categories when shown.
`canonical_url`	Canonical article URL.
`source_url`	Original input URL processed by the Bot.

How to Use Article Content Extractor

Click Run task.
Enter one or more public article URLs and a count limit.
Run the Bot and review the structured article records.
Export the result or connect it to an API, workflow builder, or agent workflow.

Input Parameters

Parameter	Required	Default	Description
`Article URLs`	Yes	`https://blog.apify.com/what-is-web-scraping/`	Public article URLs to open, one per line or separated by commas.
`Count`	Yes	`5`	Maximum number of article URLs to process.

Example Output

{
  "articles": [
    {
      "title": "What is web scraping?",
      "author": "David Barton",
      "published_date": "2024-10-15T14:32:00.000Z",
      "updated_date": "2026-07-07T12:57:19.000Z",
      "publisher": "Apify Blog",
      "article_text": "Web scraping is the process of automatically extracting data from a website...",
      "summary": "The basics of web scraping: what it is, how it works, real-world use cases, and how to get started.",
      "hero_image_url": "https://storage.ghost.io/...",
      "tags": "programming",
      "canonical_url": "https://blog.apify.com/what-is-web-scraping/",
      "source_url": "https://blog.apify.com/what-is-web-scraping/"
    }
  ]
}

How Does it Work?

How Article Content Extractor runs

BrowserAct opens each public article URL, waits for the page to stabilize, then extracts article fields in one read pass. The Bot prefers standard article metadata such as schema.org JSON-LD, Open Graph tags, canonical links, and meta descriptions, then uses visible page fallbacks when metadata is missing.

For body text, the Bot prefers clean semantic article containers before falling back to broader page text. It keeps the original URL order, stops at Count, and returns empty values for optional fields that are not visible or not exposed by the page.

How to Build a New Scraper Bot with Agent Built

Step 1: Describe the Data You Need

Open BrowserAct and start Agent Built from the Home prompt box or from Create -> Build with Agent. Paste a clear request that includes the website, the records to collect, the filters or search conditions, the fields to return, the result limit, and the inputs you want to reuse later.

Agent Built prompt templates

Step 2: Let BrowserAct Build and Test the Bot

BrowserAct explores the live website, works out the navigation and extraction path, and validates the result. If BrowserAct asks for clarification, reply in the same build conversation with the missing URL, condition, field, example, or expected output.

Agent Built exploration

Step 3: Run the Bot and Review the Result

When the build is complete, run the Bot with the default inputs first. Review the structured records, source URLs, missing fields, and row count before reusing the Bot with new inputs or connecting it to another workflow.

Agent Built run inputs

Agent Built run results

Why Use Article Content Extractor?

Article extraction helps teams build research datasets, summarize public content, enrich knowledge bases, monitor publications, and feed structured content into editorial or AI workflows. A reusable BrowserAct Bot makes the workflow repeatable without writing custom parsers for every article page.

Who Can Use This Template?

Researchers collecting public article references.
Content teams organizing editorial sources.
Analysts enriching datasets with article metadata.
Knowledge-base builders converting pages into structured records.
Automation builders sending article content into APIs, spreadsheets, or agents.

How Many Results Can You Scrape?

Use Count to control how many URLs are processed. If you provide more URLs than the count limit, the Bot stops after the requested number. If a URL is paywalled or login-gated, the Bot only captures publicly visible metadata and text.

Automation and Export

Automation and export flow

BrowserAct returns structured article records first. You can then use the output through API JSON responses, n8n workflows, Make scenarios, Zapier automations, MCP-based agent workflows, or manual CSV review depending on your downstream system.

Need Help?

Discord: Discord Community

E-mail: service@browseract.com

Brief