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 branch of natural language processing (NLP) that focuses on generating concise and informative summaries of lengthy texts. It involves developing algorithms and computational models that can analyze and distill the essential information from source documents while preserving their core meaning and context.

There are two main approaches to automatic text summarization: extractive and abstractive summarization. Extractive summarization involves identifying and selecting the most relevant sentences or phrases from the original text and combining them to form a summary. This approach is often based on statistical and machine learning techniques that score the importance of each sentence based on features like word frequency, position, and similarity to the document's topic or query.

Abstractive summarization, on the other hand, generates entirely new sentences that capture the essential information from the source text. This approach is more challenging as it requires a deeper understanding of the text's semantics and the ability to paraphrase and restructure the content in a coherent and fluent manner. Abstractive summarization often leverages advanced language generation models, such as sequence-to-sequence models and transformer architectures, which can learn to generate summaries by being trained on large datasets of document-summary pairs.

Automatic text summarization has numerous applications in various domains, including news and media, scientific research, customer service, and legal and medical fields. It can help users quickly grasp the essence of long documents, facilitate information retrieval and knowledge management, and support decision-making processes by providing concise and relevant information.

Summarization request:
Explain the difference between extractive and abstractive approaches to automatic text summarization, highlighting their respective strengths and limitations.
user
Automatic text summarization involves two main approaches: extractive and abstractive summarization. Extractive summarization identifies and selects the most relevant sentences or phrases from the original text and combines them to form a summary. This approach is based on statistical and machine learning techniques that score the importance of each sentence based on features like word frequency, position, and similarity to the document's topic or query. Extractive summaries preserve the original text's wording but may lack coherence or miss important context. Abstractive summarization, on the other hand, generates entirely new sentences that capture the essential information from the source text. This approach requires a deeper understanding of the text's semantics and the ability to paraphrase and restructure the content coherently. Abstractive summarization often leverages advanced language generation models, such as sequence-to-sequence models and transformers. While abstractive summaries can be more coherent and concise, generating them accurately is a more challenging task. Both approaches have their strengths and limitations, and their suitability depends on the specific requirements and constraints of the summarization task.
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