Summarize the key points discussed in the following passage about the evaluation of summarization techniques:

Evaluating the performance and quality of automatic text summarization systems is a crucial step in their development and deployment. Several evaluation metrics and methodologies have been proposed and employed in the field. The most commonly used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and reference human-written summaries. While ROUGE provides a quantitative measure of performance, it has limitations, as it does not capture semantic similarity or coherence.

To address these limitations, human evaluation is often employed, where human annotators assess the quality of generated summaries based on criteria such as coherence, informativeness, and faithfulness to the original text. However, human evaluation is time-consuming and subjective, making it difficult to scale and compare across different systems and datasets.

Recent efforts have focused on developing more comprehensive evaluation frameworks that combine automatic metrics with human evaluation. These frameworks aim to capture various aspects of summary quality, including factual accuracy, coherence, and relevance to the user's information needs. Additionally, task-based evaluation approaches, where the performance of summarization systems is measured based on their impact on downstream tasks, such as question answering or information retrieval, are gaining traction as they provide a more practical assessment of the system's usefulness.
Summarize the key points discussed in the following passage about the evaluation of summarization techniques:

Evaluating the performance and quality of automatic text summarization systems is a crucial step in their development and deployment. Several evaluation metrics and methodologies have been proposed and employed in the field. The most commonly used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and reference human-written summaries. While ROUGE provides a quantitative measure of performance, it has limitations, as it does not capture semantic similarity or coherence.

To address these limitations, human evaluation is often employed, where human annotators assess the quality of generated summaries based on criteria such as coherence, informativeness, and faithfulness to the original text. However, human evaluation is time-consuming and subjective, making it difficult to scale and compare across different systems and datasets.

Recent efforts have focused on developing more comprehensive evaluation frameworks that combine automatic metrics with human evaluation. These frameworks aim to capture various aspects of summary quality, including factual accuracy, coherence, and relevance to the user's information needs. Additionally, task-based evaluation approaches, where the performance of summarization systems is measured based on their impact on downstream tasks, such as question answering or information retrieval, are gaining traction as they provide a more practical assessment of the system's usefulness.
user
The key points discussed in the passage about the evaluation of summarization techniques are:

1. Evaluating the performance and quality of automatic text summarization systems is crucial for their development and deployment.
2. The most commonly used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between generated and reference summaries.
3. ROUGE has limitations as it does not capture semantic similarity or coherence.
4. Human evaluation is employed to assess summary quality based on criteria like coherence, informativeness, and faithfulness to the original text, but it is time-consuming and subjective.
5. Recent efforts focus on developing comprehensive evaluation frameworks that combine automatic metrics with human evaluation to capture various aspects of summary quality.
6. Task-based evaluation approaches, where summarization systems are evaluated based on their impact on downstream tasks like question answering or information retrieval, are gaining traction as they provide a practical assessment of the system's usefulness.
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 summarize in one sentence what made Aonuma include the Ascend ability into TOTK?

Eiji Aonuma likes to cheat. “I’m somebody who, if I can find a way, likes to do that kind of gameplay,” the longtime Zelda developer told Polygon in an interview on Thursday, ahead of The Legend of Zelda: Tears of the Kingdom’s May 12 release. It’s an ethos from which Link’s new Ascend ability was formed — as a debug feature that Aonuma and director Hidemaro Fujibayashi used to easily exit the depths of Tears of the Kingdom’s caves.

“When I was exploring the caves, I would get to the destination where I was trying to get to, and once I checked it out, I would just use the debug code to get to the top,” Fujibayashi told Polygon. “And I thought, Well, maybe this is something that can be usable in the game.”

Aonuma agreed with Fujibayashi that it was a “pain” to go all the way back through Tears of the Kingdom’s labyrinthine caves. “When I heard that, I thought, Oh, I guess he would feel the same way that I would be feeling,” Fujibayashi said. “That’s how we got to implementing Ascend. To be blunt and honest, cheating can be fun. So that’s why we decided to drop it in there.”
Response: Aonuma used ascend as a debug feature initially to get out of the caves in the game and realized it would be useful for players to have the ability as well.

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.
