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:

Text summarization is a crucial task in natural language processing that involves generating concise and informative summaries from lengthy documents or corpora. The primary goal of summarization is to capture the most salient information while significantly reducing the overall length of the text, making it easier for users to comprehend the key points and relevant details.

There are two main approaches to text summarization: extractive and abstractive. Extractive summarization is a more straightforward method that involves selecting and concatenating the most important sentences or phrases from the original text to form the summary. This approach is generally computationally efficient but may result in summaries that lack coherence or fail to capture information not explicitly stated in the text.

On the other hand, abstractive summarization aims to generate entirely new sentences that capture the essential information in a more coherent and concise manner. This approach involves understanding the underlying meaning of the text and rephrasing or restructuring the content to create a fluent and cohesive summary. Abstractive summarization is considered more challenging and often relies on advanced language models and deep learning techniques, such as sequence-to-sequence models, attention mechanisms, and transformer architectures.

Regardless of the approach, effective text summarization requires a deep understanding of the text's structure, context, and language, as well as the ability to identify and prioritize the most relevant information. Summarization algorithms and models are typically trained on large datasets of text and corresponding summaries, allowing them to learn patterns and strategies for condensing and synthesizing information effectively.

Text summarization has numerous applications across various domains, including news aggregation, academic research, legal document analysis, customer service, and more. It can help save time and effort by providing concise overviews of lengthy documents, enabling users to quickly grasp the main ideas and decide whether to read the full text or not.

Please provide a concise summary of the key points discussed in the given text.
user
The text discusses text summarization, a crucial natural language processing task focused on generating concise summaries from lengthy texts while capturing salient information. It outlines the two main approaches: extractive summarization, which selects and concatenates important sentences, and abstractive summarization, which generates new sentences capturing the essence. The text highlights the challenges involved, such as understanding context, language, and structure to identify and prioritize relevant information. It mentions that summarization models, often relying on advanced techniques like sequence-to-sequence models and transformers, are trained on datasets to learn effective condensation and synthesis strategies. Text summarization has diverse applications across domains like news, research, legal, and customer service, enabling efficient comprehension of key points from lengthy documents.
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