Summarize the key points of the following passage about the challenges of evaluating summarization systems:

Evaluating the quality of summarization systems is a significant challenge, as there is no single universally agreed-upon metric or methodology. Human evaluation, where humans judge the quality of summaries, is often considered the gold standard but is time-consuming, expensive, and can be subjective. Automated metrics, such as ROUGE, which compares the overlap between generated summaries and human-written reference summaries, are widely used but have limitations. These metrics focus on surface-level similarity and may not accurately capture semantic equivalence, coherence, or factual correctness. Additionally, they rely on the availability of high-quality reference summaries, which can be scarce or biased. More recent efforts have explored reference-less evaluation methods, such as question-answering tasks or learned evaluation models trained on human judgments. However, these approaches also have their own limitations and may not generalize well across different domains or summarization tasks. The lack of robust and reliable evaluation methods hinders the fair comparison of summarization systems and can potentially slow down progress in the field.
Summarize the key points of the following passage about the challenges of evaluating summarization systems:

Evaluating the quality of summarization systems is a significant challenge, as there is no single universally agreed-upon metric or methodology. Human evaluation, where humans judge the quality of summaries, is often considered the gold standard but is time-consuming, expensive, and can be subjective. Automated metrics, such as ROUGE, which compares the overlap between generated summaries and human-written reference summaries, are widely used but have limitations. These metrics focus on surface-level similarity and may not accurately capture semantic equivalence, coherence, or factual correctness. Additionally, they rely on the availability of high-quality reference summaries, which can be scarce or biased. More recent efforts have explored reference-less evaluation methods, such as question-answering tasks or learned evaluation models trained on human judgments. However, these approaches also have their own limitations and may not generalize well across different domains or summarization tasks. The lack of robust and reliable evaluation methods hinders the fair comparison of summarization systems and can potentially slow down progress in the field.
user
Evaluating the quality of summarization systems is a significant challenge due to the lack of a universally agreed-upon metric or methodology. Human evaluation is considered the gold standard but is time-consuming, expensive, and subjective. Automated metrics like ROUGE, which measure overlap with reference summaries, are widely used but have limitations in capturing semantic equivalence, coherence, and factual correctness, and rely on high-quality reference summaries. Recent efforts explore reference-less evaluation methods, such as question-answering tasks or learned evaluation models, but these also have limitations and may not generalize well. The lack of robust and reliable evaluation methods hinders fair comparison of systems and can potentially slow down progress in the field.
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: Can you sum up what these former survivor players said about meeting Jeff Probst in a few sentences?

"Survivor: Island of the Idols" player Elaine Stott said that although she was nervous before she met the "Survivor" host, the fact that she was sitting in front of producers and Probst "didn't really matter to me." 

"I told dirty jokes, I flirted, I cussed, I made fun of myself. That's just how I am in general," Stott told Insider. "... I said, 'Jeff, you're just a man, you put your britches on just like I do.'" 

Freberg added that the interviewers just "tell you to start talking" instead of asking direct questions, and Probst sits there "trying to size you up and trying to be intimidating."

"You just need something funny to say, so I made it up on the spot," Freberg recalled. "... I look at Jeff and I go, 'My grandma's gonna freak the f--- out when she finds out I met you.'"

Freberg added that his joke "got a big laugh," so he was "off to a good start after that."
Response: When Elaine Stott was in front of Probst and producers her nervousness went away. Freberg said the producers didn't ask questions and he was told to just start talking. Jeff sized them up, but Freeberg came up with a funny joke from the start and that put them on good terms for the rest of the interview.

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.
