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:
Text summarization is a challenging task that involves condensing a long document into a concise summary while preserving the most important information and key ideas. Several evaluation metrics are used to assess the quality of automatically generated summaries.

One commonly used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and reference summaries created by humans. ROUGE calculates the recall, precision, and F-measure based on the co-occurrence of n-grams (sequences of n words) between the generated and reference summaries.

Another metric is BLEU (Bilingual Evaluation Understudy), originally developed for evaluating machine translation systems but also used for summarization evaluation. BLEU compares the generated summary to one or more reference summaries and calculates a score based on the precision of n-gram matches.

Additional metrics include METEOR (Metric for Evaluation of Translation with Explicit ORdering), which considers not only the n-gram matches but also stemming and synonymy matching, and BERTScore, which uses pre-trained language models like BERT to assess the semantic similarity between the generated and reference summaries.

While these metrics provide quantitative measures of summary quality, human evaluation is still considered the gold standard for assessing the coherence, informativeness, and overall quality of generated summaries.

Summarization request:
Describe the commonly used evaluation metrics for text summarization and their respective approaches to assessing summary quality.
user
Commonly used evaluation metrics for text summarization include ROUGE, which measures the overlap between the generated summary and reference summaries based on n-gram co-occurrence; BLEU, which calculates a score based on the precision of n-gram matches between the generated and reference summaries; METEOR, which considers n-gram matches, stemming, and synonymy; and BERTScore, which uses pre-trained language models to assess semantic similarity. While these metrics provide quantitative measures, human evaluation is still considered the gold standard for assessing the coherence, informativeness, and overall quality of generated summaries.
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