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: Write a short summary of the passage.

This is the passage:
The most famous description of Valhalla in Old Norse literature, that of Grímnismál, portrays it as being located in Asgard, the gods’ celestial fortress.

However, other lines of evidence suggest that it was at least sometimes seen as being located underground, like the more general underworld.
Response: Grímnismál is the most famous portrayal of Valhalla, locating it in Asgard, but other theories say it was underground.

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 following paragraph about the evaluation of text summarization systems:

Evaluating the quality and effectiveness of text summarization systems is a critical aspect of their development and improvement. Various metrics and approaches have been proposed to assess the performance of summarization models. The most commonly used metrics include ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and reference summaries based on n-gram co-occurrences. Other metrics, such as BLEU (Bilingual Evaluation Understudy) and METEOR (Metric for Evaluation of Translation with Explicit ORdering), borrowed from machine translation evaluation, are also employed. Additionally, human evaluation, where human annotators rate the quality of summaries based on criteria like informativeness, coherence, and linguistic quality, is considered the gold standard for assessing summarization systems.
user
Evaluating the quality and effectiveness of text summarization systems is crucial for their development and improvement. Common evaluation metrics include ROUGE, which measures n-gram overlap between generated and reference summaries; BLEU and METEOR, borrowed from machine translation evaluation; and human evaluation, where annotators rate summaries based on informativeness, coherence, and linguistic quality. Human evaluation is considered the gold standard for assessing summarization systems.
assistant