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: Give a tl;dr for the following Reddit post - 

Questions for Parents ...

When it comes to buying books for kids, when do you decide to buy a books vs borrowing from the library?

I want my son (currently age 4) to have a collection of books but I'm trying to keep my spending under control .

I'm also finding that he is usually excited to get new books vs the ones he owns. (But this is slowly changing as I purchase his favourite ... vs the random books we've been gifted)

Right now I'm trying to follow these these rules...

Buying some of super popular books that constantly have long hold (usually in our family's favourite franchises)

Buying books my kid never wants to return

Buying books the library doesn't have and I really want him to have read

How do you manage book buying for your kids?

Do you only buy books for birthday/Christmas or buy books regularly as they move into different interests/reading levels?

Do you know how many books you own for your kids current age? (We own 20ish books that are age appropriate)
Response: I have a four-year-old son and currently buy books the library doesn't have, popular books, and books he doesn't want to return. When do you buy books for your kids instead of using libraries? 

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.

Summarize the following text, highlighting the potential future developments and applications of summarization techniques:

As natural language processing and machine learning technologies continue to advance, the field of summarization is expected to see significant developments in the coming years. One potential area of progress is the integration of multi-modal summarization, which involves summarizing information from multiple sources, such as text, images, audio, and video. This could have applications in fields like journalism, where summarizing information from various media formats could enhance news coverage and reporting. Additionally, advancements in transfer learning and few-shot learning may enable summarization models to be more easily adapted to new domains and tasks, reducing the need for large training datasets. Furthermore, the incorporation of knowledge graphs and semantic representations could enhance the ability of summarization models to capture and represent complex relationships and concepts, leading to more accurate and informative summaries. As summarization techniques continue to improve, they are likely to find applications in emerging areas such as conversational AI, where summarization could be used to provide concise and relevant information in response to user queries.
user
As natural language processing and machine learning technologies advance, the field of summarization is expected to see significant developments in the coming years. One potential area is multi-modal summarization, which involves summarizing information from multiple sources like text, images, audio, and video, with applications in journalism and enhanced news coverage. Advancements in transfer learning and few-shot learning may enable summarization models to be more easily adapted to new domains and tasks, reducing the need for large training datasets. Incorporating knowledge graphs and semantic representations could also enhance models' ability to capture complex relationships and concepts, leading to more accurate and informative summaries. Additionally, summarization techniques are likely to find applications in emerging areas like conversational AI, providing concise and relevant information in response to user queries.
assistant