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.

Read the following text carefully:

Automatic text summarization is a challenging task in the field of natural language processing (NLP). It involves using computational techniques to analyze and understand the content of a text and generate a concise summary that captures the essential information. There are two main approaches to automatic summarization: extractive and abstractive. Extractive summarization selects the most important sentences or phrases from the original text and combines them to form the summary. Abstractive summarization, on the other hand, involves generating entirely new sentences that summarize the main ideas of the text. This approach requires a deeper understanding of the text and the ability to paraphrase and restructure the information. Both extractive and abstractive methods can be implemented using various techniques, such as statistical models, machine learning algorithms, and deep learning architectures like neural networks. Automatic summarization has numerous applications, including document summarization, news article summarization, and text condensation for mobile devices or voice assistants.
user
The text discusses automatic text summarization, a challenging task in natural language processing. It involves using computational techniques to analyze and understand text content to generate concise summaries. There are two main approaches: extractive summarization, which selects and combines important sentences from the original text, and abstractive summarization, which generates new sentences summarizing the main ideas. Both approaches can be implemented using statistical models, machine learning algorithms, and deep learning architectures like neural networks. Automatic summarization has applications in document summarization, news article summarization, and text condensation for mobile devices or voice assistants.
assistant