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: In short, what are Iron Man's powers? Draw your answer from the text below:

Without his suit of armor, Tony Stark has no supernatural powers. He is only limited by his imagination. Tony is a brilliant engineer and has used his talents to create a powerful suit of armor that enables the wearer to fly, shoot beams of energy from his hands and chest, and resist the vacuum of space. The suit also protects the wearer from damage and grants superhuman strength.

The suit is constantly being redesigned to face the new challenges that Tony Stark meets on a daily basis. There are specialty suits that have been made such as the Arctic, Stealth, Space, Hulkbuster, and Thorbuster armors. There have been nearly 40 different variations of the Iron Man armor in the current reality of the Iron Man comics.
Response: In short, Iron Man has no real superpowers outside his suit. Instead, he uses his engineering skillset to create armor that grants extreme strength, flight and energy beams. His suit also protects him from damage and he regularly upgrades it to meet his needs.

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.

Given the following text on the history of summarization, summarize the key events and developments described:

The practice of summarizing texts can be traced back to ancient civilizations, where scribes and scholars would condense lengthy writings for easier dissemination and preservation. In the 15th century, the invention of the printing press facilitated the spread of written works, leading to a growing need for concise summaries.

In the 20th century, the field of automatic text summarization emerged with the advent of computers and advancements in natural language processing (NLP). Early systems relied on statistical techniques and heuristics to extract and condense sentences from texts. By the late 20th century, researchers began exploring more sophisticated methods, such as graph-based algorithms and machine learning approaches.

Today, summarization techniques have evolved significantly, with deep learning models capable of generating coherent, fluent summaries that capture the essence of complex texts. These models are trained on large datasets of text-summary pairs and can recognize patterns and relationships within the data. Summarization is widely used in various domains, including news reporting, academic research, and business intelligence, to quickly grasp the key information from vast amounts of textual data.
user
The practice of text summarization dates back to ancient times when scribes condensed lengthy writings. The invention of the printing press in the 15th century increased the need for summaries. In the 20th century, automatic summarization emerged with statistical techniques and heuristics. Later, more advanced methods like graph-based algorithms and machine learning were developed. Currently, deep learning models trained on large datasets can generate coherent summaries capturing the essence of complex texts. Summarization is widely used across domains to quickly extract key information from textual data.
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