🌍 What is Goose?

Goose is a content extraction library originally written in Java and later ported to Python.
Its main goal is to take a messy HTML page (like a news site, blog, or magazine article) and extract the main clean text content, along with useful metadata.

Think of Goose as a "content miner" ⛏️ for web pages:

• Removes ads, navigation menus, and clutter.

• Extracts the title, body text, images, meta info, and videos.

• Outputs a clean, structured format for further use in apps, NLP, or data pipelines.

⚙️ How Goose Works

When you pass a URL or raw HTML, Goose uses a series of parsing and cleaning techniques:

1. Download / Load HTML
   Goose fetches the page (if URL given) or takes raw HTML input.

2. HTML Parsing (via lxml / BeautifulSoup-like parsing)
   It breaks the HTML into a structured DOM tree.

3. Content Extraction Logic
   Goose applies heuristics to:

   • Identify the largest continuous text block (usually the main article).

   • Score content blocks based on density of text vs. links/images.

   • Filter out sidebars, menus, ads, comments, etc.

4. Metadata Extraction 🧾
   Goose extracts:

   • title → from <title> or <meta property="og:title">

   • meta description

   • canonical URL

   • tags / keywords

5. Media Extraction 🖼️
   Goose can also pull:

   • Top image (og:image, or the most relevant one in the article)

   • Embedded videos

6. Cleanup & Output
   The extracted data is returned as an Article object in Python.

🐍 Example Usage in Python

from goose3 import Goose

# Initialize Goose
g = Goose()

# Extract from a URL
article = g.extract(url="https://example.com/some-news-article")

print("Title:", article.title)
print("Meta Description:", article.meta_description)
print("Cleaned Text:\n", article.cleaned_text[:500])  # first 500 chars
print("Top Image:", article.top_image.src if article.top_image else None)

✅ Output is super clean compared to raw HTML scraping.

📦 Installation

Goose (Python port) is available as goose3 (active fork):

pip install goose3

🎯 Key Features

• Article text extraction → main body only

• Title detection

• Meta info extraction → description, keywords, canonical URL

• Image extraction → finds the most relevant image

• Language detection 🌐 (multi-language support)

• HTML noise removal

📊 Use Cases

• 📰 News Aggregators → Extract clean text for summaries.

• 📚 Content Mining → Feed into NLP pipelines (summarization, sentiment analysis).

• 🔍 SEO Tools → Extract metadata and content.

• 📖 Content Archiving → Save clean versions of articles.

• 🤖 Chatbots & AI → Provide structured knowledge from messy pages.

⚠️ Limitations

• Struggles with heavily JavaScript-rendered pages (like React/Angular apps).

• Sometimes misidentifies content if page structure is unusual.

• Development is not as active as some modern alternatives like:

  • Newspaper3k 📰 (better maintained for Python)

  • Readability.js (Node.js based)

🌟 Comparison with Similar Tools

✨ In short:
Goose is a fast, lightweight, and effective tool for article parsing and metadata extraction in Python.
If you want clean text and metadata from blogs/news sites with minimal setup, Goose is still a reliable choice. 🪿📄