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 passage carefully:

Query-based summarization is a specialized form of summarization that focuses on generating summaries tailored to specific queries or questions. Unlike traditional summarization, which aims to capture the overall gist of a text, query-based summarization targets the information most relevant to a particular question or topic of interest. This approach is particularly useful in scenarios where users need to quickly access specific information from large volumes of text without having to read through the entire content. Query-based summarization systems typically employ natural language processing techniques to analyze the query, identify relevant passages or sentences within the source text, and generate a concise summary that directly addresses the query. These systems may also leverage techniques such as keyword extraction, topic modeling, and semantic analysis to ensure the summary is relevant and informative. Query-based summarization has applications in various domains, including information retrieval, question answering systems, and knowledge management.
user
The passage discusses query-based summarization, a specialized form of summarization focused on generating summaries tailored to specific queries or questions. Unlike traditional summarization, which captures the overall gist of a text, query-based summarization targets information most relevant to a particular topic of interest. It employs natural language processing techniques to analyze the query, identify relevant passages or sentences from the source text, and generate a concise summary that directly addresses the query. Additional techniques like keyword extraction, topic modeling, and semantic analysis may be used to ensure relevance and informativeness. Query-based summarization has applications in information retrieval, question answering systems, and knowledge management, where users need quick access to specific information from large text volumes.
assistant