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: Source text:
At its simplest, forex trading is similar to the currency exchange you may do while traveling abroad: A trader buys one currency and sells another, and the exchange rate constantly fluctuates based on supply and demand.

Currencies are traded in the foreign exchange market, a global marketplace that’s open 24 hours a day Monday through Friday. All forex trading is conducted over the counter (OTC), meaning there’s no physical exchange (as there is for stocks) and a global network of banks and other financial institutions oversee the market (instead of a central exchange, like the New York Stock Exchange).

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A vast majority of trade activity in the forex market occurs between institutional traders, such as people who work for banks, fund managers and multinational corporations. These traders don’t necessarily intend to take physical possession of the currencies themselves; they may simply be speculating about or hedging against future exchange rate fluctuations.

A forex trader might buy U.S. dollars (and sell euros), for example, if she believes the dollar will strengthen in value and therefore be able to buy more euros in the future. Meanwhile, an American company with European operations could use the forex market as a hedge in the event the euro weakens, meaning the value of their income earned there falls.

Summarization request:
Briefly summarize how forex trading works and who the major players are.
Response: Forex trading involves exchanging one currency for another. The value of currencies fluctuates constantly based on supply and demand as well as economic factors. Most forex trading is done between large institutions like banks, fund managers, and multinational companies. These major players speculate on the strength of currencies or hedge against changes. They don’t usually take physical possession of the currencies.

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.

Source text: 

Automatic text summarization is a field of study within natural language processing that focuses on developing algorithms and techniques to generate concise and informative summaries from lengthy documents or text data. The goal of automatic summarization is to create a condensed version of the original text that captures the most salient information while preserving the overall meaning and context.

There are two main approaches to automatic text summarization: extractive and abstractive. Extractive summarization involves identifying and extracting the most important sentences or phrases from the source text and concatenating them to form the summary. This method relies on scoring and ranking techniques to determine the most relevant and informative sentences based on features such as word frequency, position within the text, and semantic similarity.

Abstractive summarization, on the other hand, aims to generate entirely new sentences that capture the essence of the original text, rather than simply extracting existing sentences. This approach requires a deeper understanding of the text's content and the ability to paraphrase and restructure the information. Abstractive summarization often employs advanced natural language generation techniques, such as sequence-to-sequence models and transformer architectures, to generate coherent and fluent summaries.

Automatic text summarization has numerous applications across various domains, including news article summarization, scientific paper summarization, customer feedback analysis, and document classification. It plays a crucial role in information retrieval, knowledge management, and content curation, enabling users to quickly grasp the key points and make informed decisions without needing to read through lengthy texts.
user
Automatic text summarization is a field within natural language processing that focuses on developing algorithms to generate concise summaries from lengthy texts. There are two main approaches: extractive summarization, which involves identifying and extracting the most important sentences or phrases, and abstractive summarization, which generates entirely new sentences capturing the essence of the original text. Automatic summarization has applications in news, academia, customer feedback analysis, and document classification, enabling efficient information retrieval and knowledge management.
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