Wikipedia Page Summaries Scraper

Wikipedia Page Summaries Scraper

Scrape Wikipedia page summaries from any title or search query, up to a million per run. Each summary comes with the article's title, extract, description, and thumbnail URL. No login or API key. Export to CSV, JSON, Excel, or XML.

Wikipedia's official API needs a user agent, rate-limits you, and returns nested JSON you have to flatten yourself. This reads the public REST summaries endpoint directly, filtered by title or search term, and returns each match in one fixed schema.

Who uses it	What they scrape Wikipedia for
Market researchers	Which topics a niche is discussing this week
Content marketers	Source material for articles and landing pages
Data scientists	Build a clean dataset of Wikipedia summaries for NLP
SEO analysts	Gather entity descriptions for knowledge graph enrichment
Students and academics	Collect summaries for literature reviews or study notes

What it does

This Actor collects Wikipedia page summaries by direct title or keyword search, and returns each one as a flat row.

• 🔍 Direct title lookup: paste up to a million titles, one per line, and get each summary back.
• 🔎 Keyword search: type a term like quantum computing and the Actor fetches matching summaries via Wikipedia's OpenSearch.
• 🌐 Any language: set the ISO 639-1 code (en, es, fr, de, it, ja, zh, ru, pt, ar, hi) to scrape that Wikipedia edition.
• 📄 Flat rows: every summary is returned as a simple record with title, extract, description, and thumbnail URL.
• ⚡ Bulk ready: set maxItems up to 1,000,000 per run and export to CSV, JSON, Excel, or XML.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with Wikipedia data

📈 Track topics and trends.

A market researcher runs the Actor with a list of industry terms and uses the summaries to see which concepts are gaining coverage.

📝 Build content briefs.

A content marketer scrapes summaries for a set of target keywords and uses the extracts as a starting point for article outlines.

🧠 Train NLP models.

A data scientist collects thousands of summaries in one run and feeds the flat dataset into a text classification pipeline.

🔗 Enrich knowledge graphs.

An SEO analyst pulls summaries for entity names and uses the descriptions to populate a knowledge graph or schema markup.

🎓 Study efficiently.

A student scrapes summaries for a reading list and reviews the extracts before diving into full articles.

Why choose this scraper

	What you get
No API key	Uses the public REST endpoint, no registration or OAuth
Fixed schema	Every summary returns the same flat fields, ready for your pipeline
Bulk scale	Collect up to a million summaries in a single run
Multi-language	Scrape any Wikipedia edition by language code

How it compares

No other Store actor targets Wikipedia the same way, so the honest comparison is with the alternatives teams actually weigh.

	Wikipedia Page Summaries Scraper	Build it in-house	By hand
Setup	Run it now, zero config	Days of engineering	None, but hours per pull
When Wikipedia changes	Maintained for you	You fix it	You re-learn the page
Proxies, retries, anti-bot	Built in	Your problem	Browser only
Output	Fixed JSON schema, CSV/Excel export	Whatever you build	Copy-paste
Cost	Pay per result	Engineering time	Analyst hours

Configure the run

Drive the Actor from article titles or a search query, and set a maximum number of summaries per run. The Input tab lists every parameter.

A first run with the defaults:

{
 "maxItems": 10,
 "mode": "lookup",
 "titles": "Albert Einstein\nMarie Curie\nAda Lovelace\nAlan Turing\nNikola Tesla",
 "query": "quantum computing",
 "language": "en"
}

A larger pull:

{
 "maxItems": 200,
 "mode": "lookup",
 "titles": "Albert Einstein\nMarie Curie\nAda Lovelace\nAlan Turing\nNikola Tesla",
 "query": "quantum computing",
 "language": "en"
}

Pricing

Pay-per-result: $0.01067 per result collected. You pay only for the results written to your dataset.

Results collected	Approximate cost
100 results	$1.07
1,000 results	$10.67
10,000 results	$106.70

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the Wikipedia Page Summaries Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to Wikipedia through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/wikipedia-rest-summaries-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results?

Check that your titles are spelled correctly and that the language code matches the Wikipedia edition. In search mode, try a broader query.

Why are some summaries missing an extract?

Some Wikipedia pages do not have a short extract or description. The Actor returns whatever the REST endpoint provides, which may be empty for those fields.

Why is the run slow?

Wikipedia rate limits requests. If you are scraping many titles, the Actor will pace itself. You can increase concurrency in the run settings, but be mindful of Wikipedia's policies.

Why did I get an error about the language code?

Make sure you are using a valid ISO 639-1 code, such as en, es, or fr. Some languages use two-letter codes that are not obvious, like zh for Chinese.

Why does search mode return fewer results than expected?

Wikipedia's OpenSearch returns a limited number of suggestions. For broader coverage, use lookup mode with a list of titles you already know.

FAQ

Question	Answer
Do I need a Wikipedia API key?	No. This Actor uses the public REST summaries endpoint, which does not require authentication or an API key.
How many summaries can I get in one run?	You can set maxItems up to 1,000,000. The actual number depends on how many titles or search results match.
What is the difference between lookup and search mode?	Lookup mode takes exact article titles you provide, one per line. Search mode takes a free-text query and uses Wikipedia's OpenSearch to find matching titles, then fetches their summaries.
Can I scrape Wikipedia in other languages?	Yes. Set the language field to any ISO 639-1 code, such as es for Spanish, de for German, or ja for Japanese.
What fields does each summary include?	Each row includes the article title, a short extract, a description, and a thumbnail URL when available. The exact fields are shown in the sample output.
How do I format article titles in lookup mode?	You can use spaces or underscores. For example, Albert Einstein and Albert_Einstein both work.
Can I export the results?	Yes. The Actor supports CSV, JSON, Excel, and XML exports from the Apify dataset.
Is there a rate limit?	The Actor respects Wikipedia's rate limits and includes automatic retries. If you need very high volume, consider spreading requests across multiple runs.
What if a title does not exist?	The Actor skips missing titles and continues with the rest. You can check the run log for any skipped items.
Can I use this for commercial projects?	Yes, as long as you comply with Wikipedia's terms of use and the Creative Commons license for the content.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Wikimedia Foundation, Inc. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.