Summarize the following text about text summarization evaluation metrics:

Evaluating the quality of text summaries is an important task, and several metrics have been developed for this purpose. The most commonly used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and a set of reference summaries created by humans. ROUGE has several variants, such as ROUGE-N (which measures n-gram overlap), ROUGE-L (which measures longest common subsequence), and ROUGE-SU (which considers skip-bigram co-occurrence statistics). Another widely used metric is BLEU (Bilingual Evaluation Understudy), which was originally developed for evaluating machine translation but can also be applied to text summarization. BLEU measures the n-gram precision of the generated summary against one or more reference summaries. Other metrics, such as METEOR (Metric for Evaluation of Translation with Explicit ORdering) and BERTScore, have also been proposed and used in various summarization tasks and datasets.
Summarize the following text about text summarization evaluation metrics:

Evaluating the quality of text summaries is an important task, and several metrics have been developed for this purpose. The most commonly used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and a set of reference summaries created by humans. ROUGE has several variants, such as ROUGE-N (which measures n-gram overlap), ROUGE-L (which measures longest common subsequence), and ROUGE-SU (which considers skip-bigram co-occurrence statistics). Another widely used metric is BLEU (Bilingual Evaluation Understudy), which was originally developed for evaluating machine translation but can also be applied to text summarization. BLEU measures the n-gram precision of the generated summary against one or more reference summaries. Other metrics, such as METEOR (Metric for Evaluation of Translation with Explicit ORdering) and BERTScore, have also been proposed and used in various summarization tasks and datasets.
user
The key points on text summarization evaluation metrics are: 1) ROUGE measures overlap between generated summary and reference human summaries, with variants like ROUGE-N, ROUGE-L, and ROUGE-SU. 2) BLEU measures n-gram precision of generated summary against reference summaries. 3) Other metrics like METEOR and BERTScore are also used for evaluating summarization quality.
assistant
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: What are the key points of this news article?


Apple once again posted an, until now, rare revenue decline in its latest fiscal quarter, but said its overall business improved from the December quarter and sales of its iPhones were solid.

The results reported Thursday were better than Wall Street’s muted expectations and Apple’s stock rose in after-hours trading.

The latest numbers come after the Cupertino, California, company in February posted its first quarterly revenue drop in nearly four years after pandemic-driven restrictions on its China factories curtailed sales of the latest iPhone during the holiday season.
Response: Apple announced a decline in revenue for its latest fiscal quarter. However, the results exceeded Wall Street's expectations.  Apple also had a quarterly revenue drop in February.

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.
