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: Please summarize how Asian American and Pacific Islander Heritage Month was started based on the text below: 

History
A former congressional staffer in the 1970s, Jeanie Jew, first approached Representative Frank Horton with the idea of designating a month to recognize Asian Pacific Americans, following the bicentennial celebrations.[19] In June 1977, Representatives Horton, and Norman Y. Mineta, introduced a United States House of Representatives resolution to proclaim the first ten days of May as Asian-Pacific Heritage Week.[20][21] A similar bill was introduced in the Senate a month later by Daniel Inouye and Spark Matsunaga.[20][22]

The proposed resolutions sought that May be designated for two reasons. For on May 7, 1843, the first Japanese immigrant arrived in the United States.[23][24][25] More than two decades later, on May 10, 1869, the golden spike was driven into the first transcontinental railroad, which was completed using Chinese labor.[23][24][26]

President Jimmy Carter signed a joint resolution for the celebration on October 5, 1978.[20]

On May 1, 2009, President Barack Obama signed Proclamation 8369, recognizing the month of May as Asian American and Pacific Islander Heritage Month.[1][27]
Response: A former congressional staffer with the idea approached a representative about it in the 1970s. In 1977, a resolution was introduced in the House and the Senate, and the President signed it in 1978. However, that resolution was only for the first ten days of May. It wasn't until 2009 that the whole month of May was named Asian American and Pacific Islander Heritage Month.

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 text, which discusses the evaluation of text summarization systems:

Evaluating the performance and quality of text summarization systems is a crucial aspect of their development and deployment. Several evaluation approaches and metrics have been proposed and employed, each with its own strengths and limitations.

One commonly used evaluation method is human evaluation, where human judges assess the quality of the generated summaries based on criteria such as informativeness, coherence, and faithfulness to the original text. While human evaluation provides a reliable and interpretable assessment, it can be time-consuming, subjective, and expensive, especially for large-scale evaluations.

Automated evaluation metrics, on the other hand, offer a more efficient and scalable approach to assessing summary quality. These metrics often compare the generated summaries with human-written reference summaries using techniques like n-gram overlap, semantic similarity, or question-answering capabilities. However, automated metrics may not always align perfectly with human judgments and can be influenced by factors such as length, redundancy, or grammaticality.

Recently, there has been growing interest in using task-based evaluation frameworks, where the quality of the summaries is assessed based on their effectiveness in supporting specific downstream tasks, such as information retrieval, question answering, or decision-making. This approach aims to provide a more practical and application-oriented evaluation, but it can be challenging to design and implement due to the complexity of real-world tasks and the potential for confounding factors.

Overall, the evaluation of text summarization systems remains an active area of research, with ongoing efforts to develop more robust, reliable, and interpretable evaluation methods that capture the diverse aspects of summary quality and usability across different domains and applications.
user
Evaluating the performance and quality of text summarization systems is crucial, and several approaches are employed: 1) Human evaluation, where judges assess the summaries based on criteria like informativeness and coherence, providing reliable but subjective and time-consuming assessments. 2) Automated evaluation metrics that compare generated summaries with reference summaries using techniques like n-gram overlap or semantic similarity, offering efficiency and scalability but potentially misaligning with human judgments. 3) Task-based evaluation frameworks that assess summary quality based on their effectiveness in supporting downstream tasks like information retrieval or question answering, providing practical evaluations but with implementation challenges. Overall, efforts continue to develop robust, reliable, and interpretable evaluation methods that capture diverse aspects of summary quality across various domains and applications.
assistant