Summarize the following text on the evaluation of automatic text summarization systems:

Evaluating the performance of automatic text summarization systems is a crucial task, as it helps to assess the quality and effectiveness of the generated summaries. Various evaluation methods have been developed, each with its own strengths and limitations.

One common approach is to use manual evaluation by human judges, who assess the summaries based on criteria such as informativeness, coherence, and readability. This method provides a direct measure of the summary quality but can be time-consuming and subjective, depending on the expertise and instructions provided to the judges.

Another widely used method is the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metric, which automatically compares the generated summary with one or more reference summaries created by humans. ROUGE measures the overlap of n-grams (sequences of n words) between the generated and reference summaries, providing a quantitative score based on precision, recall, and F-measure.

In addition to ROUGE, other automated metrics like BLEU, METEOR, and BERTScore have been adapted for summarization evaluation. These metrics often correlate well with human judgments but may not capture all aspects of summary quality, such as coherence and semantic accuracy.

Recently, there has been a growing interest in using question-answering and reading comprehension tasks to evaluate the informativeness and factual consistency of summaries. This involves generating questions from the source documents and assessing whether the summary contains enough information to answer those questions correctly.

Overall, the evaluation of text summarization systems is an active area of research, with ongoing efforts to develop more robust and reliable metrics that can effectively capture the various aspects of summary quality.
Summarize the following text on the evaluation of automatic text summarization systems:

Evaluating the performance of automatic text summarization systems is a crucial task, as it helps to assess the quality and effectiveness of the generated summaries. Various evaluation methods have been developed, each with its own strengths and limitations.

One common approach is to use manual evaluation by human judges, who assess the summaries based on criteria such as informativeness, coherence, and readability. This method provides a direct measure of the summary quality but can be time-consuming and subjective, depending on the expertise and instructions provided to the judges.

Another widely used method is the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metric, which automatically compares the generated summary with one or more reference summaries created by humans. ROUGE measures the overlap of n-grams (sequences of n words) between the generated and reference summaries, providing a quantitative score based on precision, recall, and F-measure.

In addition to ROUGE, other automated metrics like BLEU, METEOR, and BERTScore have been adapted for summarization evaluation. These metrics often correlate well with human judgments but may not capture all aspects of summary quality, such as coherence and semantic accuracy.

Recently, there has been a growing interest in using question-answering and reading comprehension tasks to evaluate the informativeness and factual consistency of summaries. This involves generating questions from the source documents and assessing whether the summary contains enough information to answer those questions correctly.

Overall, the evaluation of text summarization systems is an active area of research, with ongoing efforts to develop more robust and reliable metrics that can effectively capture the various aspects of summary quality.
user
The given text discusses the evaluation of automatic text summarization systems, which is crucial for assessing the quality and effectiveness of the generated summaries. It outlines various evaluation methods, including manual evaluation by human judges, who assess summaries based on criteria like informativeness, coherence, and readability. Another widely used approach is the ROUGE metric, which automatically compares the generated summary with reference summaries created by humans and measures the overlap of n-grams. Other automated metrics like BLEU, METEOR, and BERTScore are also used, but may not capture all aspects of summary quality. Recently, there has been interest in using question-answering and reading comprehension tasks to evaluate the informativeness and factual consistency of summaries. Overall, the evaluation of text summarization systems is an active area of research, with ongoing efforts to develop more robust and reliable metrics that can effectively capture various aspects of summary quality.
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: Given the following article, write a summary of the key ideas.

This is the article:
Despite the city’s support, the number of farms in Tokyo has been slowly declining for years, by nearly 14.5 percent between 2005 and 2015. Over the same period, the average age of Tokyo’s farmers increased by 3.3 years, to nearly 64. Today, Ryoto Matsuzawa says there are 1,250 farms left, most of which are under one hectare in size. “We want to preserve those at all costs.”

The city recently made a crucial decision to do so. Eighty percent of the farms protected under the Law on Productive Green Areas were registered the year that law was enacted, in 1992. The law stipulates that after 30 years, the landowners can ask the city to buy their land at market price. But most cities, strapped for cash, can’t afford to. If they don’t, the land loses its tax subsidy and can be sold for commercial development.

In other words, about 80 percent of the farms protected under the law were eligible to come up for sale in 2022. “These areas are nearly 45 percent of the agricultural land in Tokyo,” says Ryoto Matsuzawa.
Response: Between 2005 and 2015, the number of farms in Tokyo decreased by 14.5 percent and the average age of farmers increased by 3.3 years. Preserving Tokyo's remaining 1,250 small farms feels priceless to many, but there is an ongoing issue where many cities cannot afford to buy the land protected under the Productive Green Areas Law at market value, as legally required. This leads to the land being lost for agricultural use, and usually being sold for commercial development.

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.
