content.hugen.tokyo Content Summarizer

x402

content.hugen.tokyo Content Summarizer is a paid API for AI agents from content.hugen.tokyo, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15, last successful call 2026-05-02).

Fetches a URL, extracts clean article text, and returns a 2-4 sentence summary, 3-5 key points, and named entities (people, orgs, products, locations, events)

Price / call$0.02

Seller volume →—

Success90%

StatusUnknown

How this service works

AI-powered content summarization — extracts clean text from any URL then generates a concise 2-4 sentence summary, 3-5 key points, and named entity extraction (people, organizations, products, locations, events). Handles articles, blog posts, documentation, and news pages. No LLM setup or prompt engineering needed. Accepts USDC payments on Base and Solana

Output

Returns a 2-4 sentence prose summary of the article, a list of 3-5 key bullet points, and structured named entity extraction categorized into people, organizations, products, locations, and events — all derived from the cleaned article text at the provided URL.

Example use cases

Example prompt

“Can you summarize https://techcrunch.com/2024/05/01/some-article and pull out the key points and any people, organizations, or locations mentioned?”

Technical details

Endpoint

GEThttps://content.hugen.tokyo/content/summary

When to prefer

Choose this endpoint when you need a combined summary + key points + named entity extraction in a single call without managing your own LLM prompts or NLP pipeline. Ideal for quickly digesting news articles, blog posts, or documentation pages when you need both the gist and the entities mentioned. Prefer over raw scraping endpoints when you need AI-processed output rather than raw HTML or text.

Side effects

URL is unreachable or returns non-200 — endpoint cannot fetch content
URL points to a PDF or non-HTML resource not supported by the text extractor
Page is JavaScript-heavy SPA with no server-side rendered content — extracted text may be empty
Paywalled or login-gated content — extractor only sees the gate, not the article
URL contains no meaningful article text (e.g. homepage, login page) — summary may be meaningless
Payment not provided or insufficient — 402 Payment Required response

Input

{
  "url": "https://www.wikipedia.org/wiki/Machine_learning"
}

Schema · Request body

Field	Type	Description
input	—

Output

{
  "url": "https://en.wikipedia.org/wiki/Machine_learning",
  "summary": "Machine learning (ML) is a subfield of artificial intelligence focused on developing algorithms that learn from data to perform tasks without explicit programming. Rooted in statistics and mathematical optimization, ML has evolved significantly, with deep learning and neural networks currently leading performance. The field's history spans decades, with foundational concepts emerging from cognitive science and early computing experiments.",
  "entities": [
    {
      "name": "Artificial intelligence",
      "type": "org"
    },
    {
      "name": "Machine learning",
      "type": "product"
    },
    {
      "name": "Arthur Samuel",
      "type": "person"
    },
    {
      "name": "IBM",
      "type": "org"
    },
    {
      "name": "Donald Hebb",
      "type": "person"
    },
    {
      "name": "Walter Pitts",
      "type": "person"
    },
    {
      "name": "Warren McCulloch",
      "type": "person"
    },
    {
      "name": "Raytheon Company",
      "type": "org"
    },
    {
      "name": "Nils Nilsson",
      "type": "person"
    },
    {
      "name": "Duda",
      "type": "person"
    },
    {
      "name": "Hart",
      "type": "person"
    },
    {
      "name": "Tom M. Mitchell",
      "type": "person"
    },
    {
      "name": "Alan Turing",
      "type": "person"
    },
    {
      "name": "Ian Goodfellow",
      "type": "person"
    },
    {
      "name": "AlphaGo",
      "type": "product"
    },
    {
      "name": "John Hopfield",
      "type": "person"
    },
    {
      "name": "David Rumelhart",
      "type": "person"
    },
    {
      "name": "Geoffrey Hinton",
      "type": "person"
    },
    {
      "name": "Leo Breiman",
      "type": "person"
    },
    {
      "name": "DeepMind",
      "type": "org"
    },
    {
      "name": "Chinchilla 70B",
      "type": "product"
    },
    {
      "name": "NVIDIA Maxine",
      "type": "product"
    },
    {
      "name": "AIVC",
      "type": "product"
    },
    {
      "name": "OpenCV",
      "type": "product"
    },
    {
      "name": "TensorFlow",
      "type": "product"
    },
    {
      "name": "MATLAB",
      "type": "product"
    },
    {
      "name": "ECG",
      "type": "product"
    },
    {
      "name": "US",
      "type": "location"
    }
  ],
  "language": "en",
  "key_points": [
    "Machine learning algorithms learn from data to generalize to unseen data, enabling them to perform tasks without explicit programming.",
    "Deep learning, particularly using neural networks, has advanced ML capabilities, often surpassing traditional approaches.",
    "The historical development of ML is linked to early AI research, cognitive science, and statistical methods, with key figures coining terms and developing foundational theories.",
    "ML is closely related to other fields like data mining, statistics, and computer science, with overlapping methodologies and shared goals.",
    "Major approaches to ML include supervised, unsupervised, and reinforcement learning, each with distinct methods for learning from different types of data feedback."
  ],
  "word_count": 15696
}

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How this service works

Output

Technical details

Endpoint

GEThttps://content.hugen.tokyo/content/summary

When to prefer

Side effects

URL is unreachable or returns non-200 — endpoint cannot fetch content
URL points to a PDF or non-HTML resource not supported by the text extractor
Page is JavaScript-heavy SPA with no server-side rendered content — extracted text may be empty
Paywalled or login-gated content — extractor only sees the gate, not the article
URL contains no meaningful article text (e.g. homepage, login page) — summary may be meaningless
Payment not provided or insufficient — 402 Payment Required response

Input

{
  "url": "https://www.wikipedia.org/wiki/Machine_learning"
}

Schema · Request body

Field	Type	Description
input	—

Output

{
  "url": "https://en.wikipedia.org/wiki/Machine_learning",
  "summary": "Machine learning (ML) is a subfield of artificial intelligence focused on developing algorithms that learn from data to perform tasks without explicit programming. Rooted in statistics and mathematical optimization, ML has evolved significantly, with deep learning and neural networks currently leading performance. The field's history spans decades, with foundational concepts emerging from cognitive science and early computing experiments.",
  "entities": [
    {
      "name": "Artificial intelligence",
      "type": "org"
    },
    {
      "name": "Machine learning",
      "type": "product"
    },
    {
      "name": "Arthur Samuel",
      "type": "person"
    },
    {
      "name": "IBM",
      "type": "org"
    },
    {
      "name": "Donald Hebb",
      "type": "person"
    },
    {
      "name": "Walter Pitts",
      "type": "person"
    },
    {
      "name": "Warren McCulloch",
      "type": "person"
    },
    {
      "name": "Raytheon Company",
      "type": "org"
    },
    {
      "name": "Nils Nilsson",
      "type": "person"
    },
    {
      "name": "Duda",
      "type": "person"
    },
    {
      "name": "Hart",
      "type": "person"
    },
    {
      "name": "Tom M. Mitchell",
      "type": "person"
    },
    {
      "name": "Alan Turing",
      "type": "person"
    },
    {
      "name": "Ian Goodfellow",
      "type": "person"
    },
    {
      "name": "AlphaGo",
      "type": "product"
    },
    {
      "name": "John Hopfield",
      "type": "person"
    },
    {
      "name": "David Rumelhart",
      "type": "person"
    },
    {
      "name": "Geoffrey Hinton",
      "type": "person"
    },
    {
      "name": "Leo Breiman",
      "type": "person"
    },
    {
      "name": "DeepMind",
      "type": "org"
    },
    {
      "name": "Chinchilla 70B",
      "type": "product"
    },
    {
      "name": "NVIDIA Maxine",
      "type": "product"
    },
    {
      "name": "AIVC",
      "type": "product"
    },
    {
      "name": "OpenCV",
      "type": "product"
    },
    {
      "name": "TensorFlow",
      "type": "product"
    },
    {
      "name": "MATLAB",
      "type": "product"
    },
    {
      "name": "ECG",
      "type": "product"
    },
    {
      "name": "US",
      "type": "location"
    }
  ],
  "language": "en",
  "key_points": [
    "Machine learning algorithms learn from data to generalize to unseen data, enabling them to perform tasks without explicit programming.",
    "Deep learning, particularly using neural networks, has advanced ML capabilities, often surpassing traditional approaches.",
    "The historical development of ML is linked to early AI research, cognitive science, and statistical methods, with key figures coining terms and developing foundational theories.",
    "ML is closely related to other fields like data mining, statistics, and computer science, with overlapping methodologies and shared goals.",
    "Major approaches to ML include supervised, unsupervised, and reinforcement learning, each with distinct methods for learning from different types of data feedback."
  ],
  "word_count": 15696
}