{"dataStatus":{"requestParameter":"url_host=gugudata.github.io&url_path=/gugudata-io/guides/article-content-extraction-api-seo-guide/","statusCode":200,"status":"SUCCESS","statusDescription":"successfully","responseDateTime":"2026-11-07 17:01:14+0000","dataTotalCount":1},"data":{"url":"https://gugudata.github.io/gugudata-io/guides/article-content-extraction-api-seo-guide/","title":"Article Content Extraction API Integration Guide | GuGuData.io Guides","description":"Extract LLM-ready article text, readable HTML, metadata, images, author, publication time, and source for RAG, search, monitoring, and analysis.","links":["https://gugudata.io/details/fetchcontent","https://gugudata.io/demo/fetchcontent","https://gugudata.io/details/search-visibility","https://gugudata.io/"],"image":"","imageAlt":"","images":[],"content":"

Article Content Extraction API Integration Guide

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Search teams, content platforms, and data products often start with the same messy input: a public article URL. The page may include navigation, cookie banners, related posts, comments, scripts, and layout HTML. What you usually need for an SEO workflow is much smaller and much more structured: the article title, description, readable body, plain text, main image, image candidates, author, published time, and source domain.

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The GuGuData Article Content Extraction API is designed for that exact job. It extracts clean article content from a public webpage URL and returns a normalized JSON response that can be stored, indexed, summarized, compared, or passed into downstream SEO automation.

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This guide explains where the API fits in an SEO stack, how to call it, what fields to expect, and how to combine it with related GuGuData website tools.

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Start here: Try the live demo or view the current API details.

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Why article extraction matters for SEO

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Modern SEO work is not only about ranking pages. Teams also need to monitor competitors, build internal content intelligence, analyze topic coverage, enrich editorial workflows, and turn public pages into structured records.

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Article extraction helps with:

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The key point is consistency. If your pipeline stores raw HTML from every page, every downstream step has to deal with layout noise. If your pipeline stores a clean article record, later analysis becomes much easier.

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API overview

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The endpoint accepts a public HTTP or HTTPS article URL and returns article-level fields. It is useful when you want a focused article parser instead of a broad page-to-Markdown or page-to-HTML conversion.

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Request parameters

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ParameterTypeRequiredDescription
appkeystringYesYour GuGuData application key. Send it as a query parameter.
urlstringYesPublic HTTP or HTTPS article URL. Send it in the JSON body.
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Example request:

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curl -X POST \"https://api.gugudata.io/v1/websitetools/fetchcontent?appkey=YOUR_APPKEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"url\": \"https://gugudata.github.io/gugudata-io/guides/article-content-extraction-api-seo-guide/\"\n  }'
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Response fields

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FieldTypeDescription
urlstringFinal article URL after redirects.
titlestringExtracted article title when available.
descriptionstringArticle summary or page description when available.
contentstringReadable article HTML.
contentTextstringPlain text with article paragraph boundaries preserved as line breaks.
imagestringPrimary article image URL when available.
imagesarray<object>Article image candidates with source URL, absolute URL, alt text, width, and height.
authorstringArticle author when available.
publishedstringNormalized published time when available, typically YYYY-MM-DD HH:mm.
sourcestringSource domain or publisher.
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Example response shape:

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{\n  \"dataStatus\": {\n    \"statusCode\": 200,\n    \"status\": \"SUCCESS\",\n    \"statusDescription\": \"successfully\",\n    \"dataTotalCount\": 1\n  },\n  \"data\": {\n    \"url\": \"https://example.com/articles/final\",\n    \"title\": \"Example article title\",\n    \"description\": \"A short page description\",\n    \"content\": \"<article><p>Readable article HTML...</p></article>\",\n    \"contentText\": \"Readable article text...\\nA second paragraph...\",\n    \"image\": \"https://example.com/cover.jpg\",\n    \"images\": [\n      {\n        \"src\": \"/cover.jpg\",\n        \"absoluteUrl\": \"https://example.com/cover.jpg\",\n        \"alt\": \"Article cover\",\n        \"width\": \"1200\",\n        \"height\": \"630\"\n      }\n    ],\n    \"author\": \"Example Author\",\n    \"published\": \"2026-07-08 00:00\",\n    \"source\": \"example.com\"\n  }\n}
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SEO workflows you can build

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1. Competitor content monitoring

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Store a list of competitor article URLs, extract each page on a schedule, and compare changes in title, description, body length, image usage, and publication freshness. This is useful for tracking fast-moving topics where search results change weekly.

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A practical database record can include:

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Once content is normalized, you can build alerts for newly published content, title changes, or unusually long and short articles in a target topic cluster.

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2. Topic coverage and content gap analysis

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For a content gap workflow, extract the top pages for a query, then compare their body text against your own content. The goal is not to copy text. The goal is to understand which concepts, subtopics, and entities appear repeatedly across ranking pages.

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The Article Content Extraction API gives you the clean contentText needed for:

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Pair this with Search Visibility API when you also need to understand how visible a URL or query group is in search.

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3. AI and knowledge-base ingestion

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Large language model workflows perform better when the input text is clean. Instead of sending full page HTML into an AI step, extract the article first and pass title, description, and contentText into your prompt.

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For example:

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{\n  \"title\": \"Example article title\",\n  \"description\": \"A short page description\",\n  \"contentText\": \"Readable article body...\"\n}
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This helps reduce prompt noise, avoids navigation text, and makes outputs easier to audit.

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4. Editorial QA

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Content teams can use extracted article records to check whether a published page has the expected title, description, author, publish date, and hero image. That makes the API useful not only for external research, but also for internal publishing checks.

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Common QA checks include:

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GuGuData has several URL-focused website tools. Use the extraction endpoint that matches your output.

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Use Article Content Extraction when the source is an article and the target is a reusable content record. Use URL to Markdown when the result will be read by humans or stored in documentation. Use URL to JSON when your use case needs custom fields that are not fixed in the article parser response.

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Implementation notes

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HTTP status handling

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HTTP statusMeaningRecommended handling
200Article content extracted.Parse data and store the fields your workflow needs.
400Missing or invalid URL.Validate URL format and request body.
401Missing or unknown application key.Check your appkey.
403Access or payment issue.Check subscription and endpoint access.
429Rate limit reached.Reduce concurrency or retry later.
422The page is not HTML, has no extractable article body, is too large, or redirects too many times.Review the target page and use an endpoint suited to its content type.
502The target website timed out, failed at the network layer, or returned a server error.Retry conservatively and keep the failed URL for review.
503The extraction service is temporarily unavailable.Retry later without changing the request.
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FAQ

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Is this a crawler?

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No. This endpoint extracts content from a URL you provide. Your application should still manage its own URL list, queue, scheduling, and retry rules.

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Can I use it for every page on a site?

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Use it for article-like pages where title, body text, metadata, and image candidates matter. For broad site audits, combine it with URL discovery, link extraction, and technical SEO checks.

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Does it return both HTML and text?

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Yes. content provides readable article HTML, while contentText provides plain text for search, indexing, classification, or AI workflows.

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How is this different from URL to Markdown?

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Article Content Extraction returns a structured article object. URL to Markdown returns a Markdown representation of the page. Use the article API for structured data and URL to Markdown for portable readable documents.

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For more website and SEO APIs, visit GuGuData.

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