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 does this article say Abyssinian ground hornbills typically eat in their natural habitat?

Primarily carnivores, Abyssinian ground hornbills eat reptiles such as cobras, tortoises and lizards, as well as mammals like hares and mongooses, and insects and arachnids. Occasionally they will scavenge for carrion to supplement their diet, as well as consume seeds and fruit. Abyssinian ground hornbills will travel up to seven miles (11 kilometers) each day in search of food, overtaking and consuming prey they encounter. These animals have been observed following ungulate herds and forest fires to feed on prey fleeing disturbances.
At the Smithsonian's National Zoo, Abyssinian ground hornbills are given Nebraska Brand Feline Diet, mice, mealworms and crickets.
Response: Abyssinian ground hornbills typically eat reptiles, mammals, and insects as they are carnivores, but they occasionally eat carrion, seeds, and fruit.  

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 key points made in the following passage about the history and techniques of summarization:

Summarization is the process of condensing a larger piece of text into a concise summary, capturing only the core or most important elements. The practice dates back centuries, with early examples found in epitomes of classical works like the Iliad and Odyssey summaries created during the Roman era. Summarization techniques have evolved over time, from manual abridgment to modern computational methods.

One early summarization approach was the 'extract' method, which identified and extracted the most salient sentences from the original text. More advanced 'abstractive' techniques involve interpreting the semantic meaning to generate new sentences conveying the key points in novel phrasing. Contemporary neural summarization models leverage deep learning on large datasets to produce concise abstracts.

While extractive summarization remains valuable for its simplicity, state-of-the-art summarization systems employ more sophisticated abstractive methods to produce high-quality, coherent summaries tailored to the user's needs. Summarization is now essential across many domains requiring information synthesis, from research and journalism to legal documentation and customer service. As AI and natural language processing capabilities advance, the scope and applications of automatic summarization continue to expand.
user
The passage explains that summarization is the process of condensing a larger text into a concise summary capturing only the core elements. It traces the origins of summarization to ancient epitomes and describes two main techniques: extractive (extracting salient sentences) and abstractive (interpreting meaning to generate new sentences). While extractive methods are simpler, modern neural summarization models employ advanced abstractive techniques leveraging deep learning to produce high-quality summaries tailored to user needs. Summarization is now essential across many domains requiring information synthesis, and its applications continue to expand with AI advancements.
assistant