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 from the following text about the history and development of automatic summarization techniques:

Automatic text summarization is the process of creating a concise and precise summary of a longer text document. This task has long been a goal of natural language processing (NLP) researchers, as being able to automatically summarize text could unlock many applications and benefits. Early work in the 1950s and 1960s focused on extracting key sentences from documents based on heuristic rules and statistical models. In the 1990s, researchers started applying machine learning techniques like Naive Bayes and decision trees to learn what makes a good summary. The rise of large language models and deep learning in the 2010s led to more advanced neural network architectures for summarization like sequence-to-sequence models and transformer-based models. Current state-of-the-art systems use pre-trained language models fine-tuned on summarization datasets and can generate high-quality abstractive summaries by understanding context and rephrasing key information. However, there are still challenges around faithfulness, consistency, and ability to summarize very long documents. Summarization remains an active area of NLP research focused on improving performance and enabling real-world applications across many domains.
user
This text outlines the history and evolution of automatic text summarization techniques. Early work used heuristic rules and statistical methods to extract key sentences. Machine learning was applied in the 1990s. More recently, large language models and neural networks like transformers have enabled high-quality abstractive summarization by understanding context and rephrasing content. However, challenges remain in terms of faithfulness, consistency, and handling long documents.
assistant