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: Please summarize how Asian American and Pacific Islander Heritage Month was started based on the text below: 

History
A former congressional staffer in the 1970s, Jeanie Jew, first approached Representative Frank Horton with the idea of designating a month to recognize Asian Pacific Americans, following the bicentennial celebrations.[19] In June 1977, Representatives Horton, and Norman Y. Mineta, introduced a United States House of Representatives resolution to proclaim the first ten days of May as Asian-Pacific Heritage Week.[20][21] A similar bill was introduced in the Senate a month later by Daniel Inouye and Spark Matsunaga.[20][22]

The proposed resolutions sought that May be designated for two reasons. For on May 7, 1843, the first Japanese immigrant arrived in the United States.[23][24][25] More than two decades later, on May 10, 1869, the golden spike was driven into the first transcontinental railroad, which was completed using Chinese labor.[23][24][26]

President Jimmy Carter signed a joint resolution for the celebration on October 5, 1978.[20]

On May 1, 2009, President Barack Obama signed Proclamation 8369, recognizing the month of May as Asian American and Pacific Islander Heritage Month.[1][27]
Response: A former congressional staffer with the idea approached a representative about it in the 1970s. In 1977, a resolution was introduced in the House and the Senate, and the President signed it in 1978. However, that resolution was only for the first ten days of May. It wasn't until 2009 that the whole month of May was named Asian American and Pacific Islander Heritage Month.

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

Automatic summarization is the process of shortening a set of data computationally to create a subset (a summary) that represents the most important or relevant information within the original content. Summarizing text has existed for centuries as a technique for making information more concise and accesssible, with scribes and scholars manually summarizing important documents. However, the rise of the digital age and the subsequent explosion of data has made manual text summarization nearly impossible at a large scale. This has led to the development of automatic summarization techniques using computational methods.

Early work on automatic summarization focused on extracting sentences from a text and concatenating them to form summaries. This evolved into more sophisticated methods that considered other textual units like paragraphs or sections. More recent approaches use deep learning models, such as neural networks and transformer models like BERT, to generate abstractive summaries which do not simply extract text but paraphrase and restructure the information in a coherent way.

The field of automatic summarization continues to advance with ongoing research exploring evaluation methods, multi-document summarization, and specialized summarization for domains like scientific literature, social media content, and spoken dialogue. Key challenges include generating coherent, accurate, and unbiased summaries and being able to customize summaries for different purposes and audiences.
user
Automatic summarization is the computational process of condensing large amounts of text into concise summaries. Early techniques focused on extracting and concatenating key sentences, evolving into more advanced approaches using deep learning models to generate abstractive summaries that paraphrase and restructure the original information. The field continues to advance, addressing challenges like coherence, accuracy, bias, multi-document summarization, and specialized summarization for different domains and purposes.
assistant