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: In short, what does this paragraph say about cleansers? Here is the text:
A mild cleanser: When selecting a cleanser, choose one for your skin type. Dr. Katta says, “For example, if you have acne-prone skin, use a mild cleanser that removes oil.” Dry skin does better with a hydrating cleanser.

To get the best results from your cleanser, wash your face twice a day – when you get up and before bed. You also want to wash after sweating.
Response: It says to choose a cleanser based on your skin type and to wash your face before and after bed and after sweating.

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 this text about challenges in summarization evaluation:

Assessing the quality of automatic summaries is a key challenge in text summarization research. Commonly used metrics like ROUGE measure n-gram overlap between system and reference summaries, but have limitations in capturing factors like coherence, consistency with the source, and overall quality. Manual evaluation by humans is more reliable but costly and subjective. Newer approaches like PYRAMID and question-answering based metrics attempt to incorporate deeper semantic understanding. However, there is still no single universally accepted automatic evaluation method that perfectly correlates with human judgments across different data sets and domains. Hybrid techniques combining statistical measures with manual assessments may provide a balanced evaluation approach as research continues to advance evaluation methods along with summarization models.
user
The passage discusses challenges in evaluating the quality of automatically generated summaries. While metrics like ROUGE are commonly used, they have limitations in capturing aspects like coherence and consistency. Manual human evaluation is more reliable but subjective and costly. Newer approaches like PYRAMID and question-answering attempt deeper semantic evaluation, but there is still no universally accepted automatic metric that perfectly aligns with human judgments across domains. A hybrid approach combining statistical and manual methods may provide a balanced evaluation as research continues to improve evaluation techniques.
assistant