Summarize the following text:

Automatic text summarization is a branch of natural language processing (NLP) and machine learning that aims to create concise summaries of longer texts using algorithms and computational methods. There are two main approaches to automatic summarization: extractive and abstractive. Extractive summarization involves identifying and extracting the most important sentences or passages from the original text and combining them to form a summary. Abstractive summarization, on the other hand, generates entirely new sentences to capture the main ideas of the source text, often using techniques like semantic analysis and language generation models. Both approaches have their advantages and challenges, and researchers continue to develop more advanced algorithms and models to improve the quality and accuracy of automatic summaries.
Summarize the following text:

Automatic text summarization is a branch of natural language processing (NLP) and machine learning that aims to create concise summaries of longer texts using algorithms and computational methods. There are two main approaches to automatic summarization: extractive and abstractive. Extractive summarization involves identifying and extracting the most important sentences or passages from the original text and combining them to form a summary. Abstractive summarization, on the other hand, generates entirely new sentences to capture the main ideas of the source text, often using techniques like semantic analysis and language generation models. Both approaches have their advantages and challenges, and researchers continue to develop more advanced algorithms and models to improve the quality and accuracy of automatic summaries.
user
Automatic text summarization is a field in natural language processing that uses algorithms and computational methods to generate concise summaries of longer texts. It involves two main approaches: extractive summarization, which identifies and extracts key sentences or passages, and abstractive summarization, which generates new sentences to capture the main ideas. Researchers continue to develop more advanced techniques to improve the quality and accuracy of automatic summaries.
assistant
Generate a series of (instruction, response) pairs that are similar in context and structure to the example provided below. Each pair should consist of a concise instruction followed by an appropriate, detailed response. The instruction should pose a clear task or question, while the response should provide a comprehensive answer or solution that could be understood by someone with a basic understanding of the subject.

Example pair: 
Instruction: Read the article below:

Middle-of-funnel content

At this next stage, your target audience is now aware of your business and the solution you provide, and they’re interested.

They trust you enough to make slightly bigger commitments, like signing up for a webinar and engaging with longer-form content; and they’re interested enough in your offering to read brand-specific material like case studies, testimonials, and newsletter emails.

Their goal is to learn more about the solution you provide—both in general and the one specifically offered by your business.

Your goal is to educate them on your offering and set your business apart from competitors—not just with your features and benefits but with your expertise and knowledge.

Next, write a summary of what it says about Middle-of-funnel content
Response: Middle-of-funnel content includes brand-specific content that educates interested audiences that are already considering an offer.

Your task is to generate more pairs that maintain this level of clarity and detail. The topic is Summarization. Write a long text of instruction by yourself, then summarize the given instruction in a response. Ensure that the responses are informative and accurate, suitable for an educational context. 

Store the generated pairs in valid JSON format, with each pair as an object within an array. Each object should have two key-value pairs: "instruction" and "response". For instance: 

{"contents": [{"instruction": "text", "response": "text"}, {"instruction": "text", "response": "text"}, ...]} 

Remember to maintain consistency in the format and ensure the generated pairs are diverse and cover a broad range of subjects. You must return the response in the asked format and you must not add any additional text in your response.
