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: Summarize the use of color in decorating. Here is the link:
Color
Choosing a color palette is an essential part of interior design. While you could have a different style and color scheme in each room, often you will want to tie the whole dwelling together. Consider the right colors for a small room as well as colors and patterns for a large room.
Response: In interior design, not only do you want to choose the right colors for individual rooms, you want to use colors that tie the whole dwelling together.

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 advantages and challenges of automatic text summarization systems. Here is the long text: Automatic text summarization systems have become increasingly important for processing the vast amounts of textual data available today. These systems use natural language processing and machine learning techniques to generate concise summaries from lengthy documents or collections. One key advantage is that they can quickly process large volumes of text, saving time and effort compared to manual summarization. They are also consistent and unbiased in their analysis. However, these systems face challenges like accurately representing context, capturing nuances, and generating fluent, coherent summaries. They may also struggle with specialized domains or low-quality source texts. While automatic summarization is a powerful tool, it still requires human oversight and improvement.
user
Automatic text summarization systems have advantages like quickly processing large volumes of text in a consistent, unbiased manner. However, they face challenges like accurately representing context, capturing nuances, generating fluent summaries, and handling specialized domains or low-quality texts. While useful, these systems still require human oversight and ongoing improvement.
assistant