Article Data Extractor API

This API is perfect for those that want to retrieve structured data from an article on the web. Only with the URL will you receive an extensive list of information. Try it out!

About the API:

With Article Data Extractor you will be able to scrape and retrieve all the relevant information from any article you find on the web. Forget about ads, banners and other unessential parts as well. Only receive all the data related to the article of your choice. 

 

What your API receives and what your API provides (input/output)?

Article Data Extractor takes only 1 parameter — the URL of any article or blog. It scrapes and extracts any relevant information such as title, text, published time, media links, and many more. Save time and receive all this data structured so you can filter, query, and store all the information that the web has for you. 

 

What are the most common uses cases of this API?

This API is perfect for any marketing agency or any news platform that wants to retrieve the most important information from an article. This is the author's name, the text from the article itself, and do not forget about TAGS. With this API all the tags embedded in the article will be available. 

Also, this is great to compare what images are using other blogs or news forums in different articles. 

So, if you have a large collection of articles, you will be able to filter by author's name, by tag elements, or even by published dates. This API will help you to have your articles better organized. }

 

Are there any limitations with your plans?

Besides API call limitations per month:

  • Basic: 3 requests per second.
  • Pro: 5 requests per second. 

API Documentation

Endpoints


Version 2.0 will allow you to parse any article of your choice. 

Extract main article and metadata from a news entry or blog post.

 
 


                                                                            
GET https://zylalabs.com/api/35/article+data+extractor+api/1880/article+data+extractor
                                                                            
                                                                        

Article Data Extractor - Endpoint Features

Object Description
url [Required] The URL of the article.
Test Endpoint

API EXAMPLE RESPONSE

       
                                                                                                        
                                                                                                                                                                                                                            {"error":0,"message":"Article extraction success","data":{"url":"https://www.meritalk.com/articles/nasa-cites-ai-work-with-ibm-to-address-global-climate-challenges/","title":"NASA Cites AI Work With IBM to Address Global Climate Challenges","description":"The capabilities of these use cases, Gentemann said, are changing the ways that weather, climate, and environmental monitoring is being conducted.\n“We’ve now just released the weather and climate model based on data that has two billion parameters,” said Gentemann.\n“We’re now looking at … 40 years of weather and climate data.\nAfter AI systems process data, complex information like orbital dynamics, infrared, microwave data, and physical and thermodynamic models is then interpreted by NASA scientists.\nOther challenges NASA faces involve the sustainability of AI models and infrastructure....","links":["https://www.meritalk.com/articles/nasa-cites-ai-work-with-ibm-to-address-global-climate-challenges/"],"image":"https://www.meritalk.com/wp-content/uploads/2018/11/NASA-1-min.jpg","content":"<div><p>The National Aeronautics and Space Administration (NASA) is leveraging the power of artificial intelligence (AI) technologies to tackle critical climate and environmental challenges, with a focus on making data more accessible and efficient.&#160;</p>\n<p>Speaking at the <a href=\"https://meritalk.com/event/think-leadership-exchange-washington-dc/?campaign=ed-11-20-24\" target=\"_blank\" rel=\"noopener\"><b>IBM Think Leadership Exchange </b></a>in Washington, D.C., on Nov. 20, Chelle Gentemann, the open science program officer at NASA, said that the agency has been using AI models for various use cases including identifying flooding in India using large-scale satellite and geospatial data, and predicting locust breeding grounds in Africa using open-source models.&#160;&#160;</p>\n<p>The capabilities of these use cases, Gentemann said, are changing the ways that weather, climate, and environmental monitoring is being conducted.&#160;&#160;</p>\n<p>&#8220;We&#8217;ve now just released the weather and climate model based on data that has two billion parameters,&#8221; said Gentemann. &#8220;We&#8217;re now looking at &#8230; 40 years of weather and climate data. And within the encoder we&#8217;ve really fine tuned it for gap filling, for high resolution, but also for time. So, we can do both environmental monitoring but can also do prediction.&#8221;&#160;</p>\n<p>Working with partners such as IBM, Gentemann said that NASA has been developing certain AI technologies &#8211; such as encoders that handle transforming raw data. The challenge scientists face, she added, is not a lack of data but extracting meaningful knowledge from massive datasets, which AI helps with.&#160;&#160;</p>\n<p>After AI systems process data, complex information like orbital dynamics, infrared, microwave data, and physical and thermodynamic models is then interpreted by NASA scientists.&#160;</p>\n<p>&#8220;Bringing the scientific expertise together with the AI expertise, so that as we&#8217;re developing encoders [and] we know where our strengths are, what might be hallucinated &#8211; we can provide value and guidance to the next user, so that &#8230;[they] know what can be done accurately with these models,&#8221; said Gentemann.&#160;</p>\n<p>Other challenges NASA faces involve the sustainability of AI models and infrastructure. One way to mitigate the high costs of powering AI systems is turning to hardware innovations, said Priya Nagpurkar, the vice president of hybrid cloud and AI platform at IBM Research.&#160;&#160;</p>\n<p>&#8220;There&#8217;s now increasingly an awareness of energy efficiency [of AI systems],&#8221; said Nagpurkar. &#8220;It&#8217;s not just cost per millions of tokens, it&#8217;s also cost per millions of tokens per watt. And can we improve on that &#8211; especially working on climate science, this seems to be an equally important concern. So, we can see here that hardware innovations are going to be essential.&#8221;&#160;</p>\n<p>Continuing to have open-source models is a key tool in helping people access and interpret data, Gentemann added, making it more usable and beneficial as a public resource.&#160;</p>\n<p>&#8220;Federal agencies, we are interested in the public good,&#8221; said Gentemann. &#8220;We know that openness is going to reap them a benefit, it&#8217;ll be the most efficient and it will have the most benefit for the public good. And with a lot of different partners, you have to look beyond &#8230; what people say and what they actually do, and I think that&#8217;s one of the reasons that this [IBM] partnership works so well &#8211; is this deep held belief in the value of openness for public good.&#8221;&#160;</p>\n</div>","author":"Weslan Hansen, Weslan Hansen Is A Meritalk Staff Reporter Covering The Intersection Of Government","favicon":"https://www.meritalk.com/wp-content/themes/meritalk.com/favicon.ico","source":"www.meritalk.com","published":"Unknown Date","ttr":2.53,"plain_text":"The National Aeronautics and Space Administration (NASA) is leveraging the power of artificial intelligence (AI) technologies to tackle critical climate and environmental challenges, with a focus on making data more accessible and efficient.\n\nSpeaking at the IBM Think Leadership Exchange in Washington, D.C., on Nov. 20, Chelle Gentemann, the open science program officer at NASA, said that the agency has been using AI models for various use cases including identifying flooding in India using large-scale satellite and geospatial data, and predicting locust breeding grounds in Africa using open-source models.\n\nThe capabilities of these use cases, Gentemann said, are changing the ways that weather, climate, and environmental monitoring is being conducted.\n\n“We’ve now just released the weather and climate model based on data that has two billion parameters,” said Gentemann. “We’re now looking at … 40 years of weather and climate data. And within the encoder we’ve really fine tuned it for gap filling, for high resolution, but also for time. So, we can do both environmental monitoring but can also do prediction.”\n\nWorking with partners such as IBM, Gentemann said that NASA has been developing certain AI technologies – such as encoders that handle transforming raw data. The challenge scientists face, she added, is not a lack of data but extracting meaningful knowledge from massive datasets, which AI helps with.\n\nAfter AI systems process data, complex information like orbital dynamics, infrared, microwave data, and physical and thermodynamic models is then interpreted by NASA scientists.\n\n“Bringing the scientific expertise together with the AI expertise, so that as we’re developing encoders [and] we know where our strengths are, what might be hallucinated – we can provide value and guidance to the next user, so that …[they] know what can be done accurately with these models,” said Gentemann.\n\nOther challenges NASA faces involve the sustainability of AI models and infrastructure. One way to mitigate the high costs of powering AI systems is turning to hardware innovations, said Priya Nagpurkar, the vice president of hybrid cloud and AI platform at IBM Research.\n\n“There’s now increasingly an awareness of energy efficiency [of AI systems],” said Nagpurkar. “It’s not just cost per millions of tokens, it’s also cost per millions of tokens per watt. And can we improve on that – especially working on climate science, this seems to be an equally important concern. So, we can see here that hardware innovations are going to be essential.”\n\nContinuing to have open-source models is a key tool in helping people access and interpret data, Gentemann added, making it more usable and beneficial as a public resource.\n\n“Federal agencies, we are interested in the public good,” said Gentemann. “We know that openness is going to reap them a benefit, it’ll be the most efficient and it will have the most benefit for the public good. And with a lot of different partners, you have to look beyond … what people say and what they actually do, and I think that’s one of the reasons that this [IBM] partnership works so well – is this deep held belief in the value of openness for public good.”","ttr_disclaimer":"Assuming 200 wpm reading speed"}}
                                                                                                                                                                                                                    
                                                                                                    

Article Data Extractor - CODE SNIPPETS


curl --location --request GET 'https://zylalabs.com/api/35/article+data+extractor+api/1880/article+data+extractor?url=https://www.thestartupfounder.com/use-this-data-extractor-api-to-get-article-data-from-mathrubhumi/' --header 'Authorization: Bearer YOUR_API_KEY' 


    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Article Data Extractor API REST API, simply include your bearer token in the Authorization header.
Headers
Header Description
Authorization [Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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The Article Data Extractor API is designed to extract relevant information from articles or blogs by providing the URL of the desired webpage. It scrapes and retrieves data such as the article's title, text, published time, media links, and more. The API aims to save time by delivering structured data that can be easily filtered, queried, and stored for further use.

The Article Data Extractor API can extract various types of information from articles or blogs. This includes the article's title, main text content, published time, media links (such as images or videos embedded within the article), and potentially other metadata associated with the article.

The accuracy of data extraction depends on factors such as the structure and quality of the webpage, as well as the consistency of its layout and formatting. The API employs scraping techniques to retrieve information, and its accuracy may vary based on these factors. However, it is designed to provide reliable and relevant data from the provided article or blog URL.

No, at the moment batch requests are not supported. You will have to make one API call per article that you want to extract the data from.

The extracted data from the articles or blogs is typically returned in a structured format, such as JSON. This makes it easier to work with the data programmatically, as you can access specific fields and properties. The API organizes the extracted information in a structured manner, allowing you to filter, query, and store the data as per your requirements.

Zyla API Hub is like a big store for APIs, where you can find thousands of them all in one place. We also offer dedicated support and real-time monitoring of all APIs. Once you sign up, you can pick and choose which APIs you want to use. Just remember, each API needs its own subscription. But if you subscribe to multiple ones, you'll use the same key for all of them, making things easier for you.

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