Evaluation metrics are used to assess the quality and effectiveness of automatic text summarization systems. Read the following text and summarize the key points about evaluation metrics for summarization:

Evaluating the quality of automatic text summaries is a crucial but challenging task. Several metrics have been proposed to measure the performance of summarization systems, each with its own strengths and limitations. Some commonly used metrics include ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures overlap between the system summary and human-written reference summaries based on n-gram co-occurrence statistics. Other metrics like BLEU (Bilingual Evaluation Understudy) and METEOR (Metric for Evaluation of Translation with Explicit Ordering) were originally developed for machine translation but have been adapted for summarization evaluation. However, these metrics primarily focus on lexical similarity and may not fully capture other important aspects of summary quality, such as coherence, informativeness, and faithfulness to the original content. Human evaluation remains the gold standard but is time-consuming and subjective. Recent efforts have explored more advanced evaluation techniques using question-answering and natural language inference frameworks.
Evaluation metrics are used to assess the quality and effectiveness of automatic text summarization systems. Read the following text and summarize the key points about evaluation metrics for summarization:

Evaluating the quality of automatic text summaries is a crucial but challenging task. Several metrics have been proposed to measure the performance of summarization systems, each with its own strengths and limitations. Some commonly used metrics include ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures overlap between the system summary and human-written reference summaries based on n-gram co-occurrence statistics. Other metrics like BLEU (Bilingual Evaluation Understudy) and METEOR (Metric for Evaluation of Translation with Explicit Ordering) were originally developed for machine translation but have been adapted for summarization evaluation. However, these metrics primarily focus on lexical similarity and may not fully capture other important aspects of summary quality, such as coherence, informativeness, and faithfulness to the original content. Human evaluation remains the gold standard but is time-consuming and subjective. Recent efforts have explored more advanced evaluation techniques using question-answering and natural language inference frameworks.
user
Evaluating automatic text summaries is challenging, and various metrics have been proposed. Common metrics include ROUGE, which measures n-gram overlap with human references, and BLEU and METEOR adapted from machine translation. However, these mainly focus on lexical similarity and may not fully capture coherence, informativeness, and faithfulness. Human evaluation is the gold standard but subjective and time-consuming. Recent efforts explore advanced evaluation techniques using question-answering and natural language inference frameworks.
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: Read the article below:

Middle-of-funnel content

At this next stage, your target audience is now aware of your business and the solution you provide, and they’re interested.

They trust you enough to make slightly bigger commitments, like signing up for a webinar and engaging with longer-form content; and they’re interested enough in your offering to read brand-specific material like case studies, testimonials, and newsletter emails.

Their goal is to learn more about the solution you provide—both in general and the one specifically offered by your business.

Your goal is to educate them on your offering and set your business apart from competitors—not just with your features and benefits but with your expertise and knowledge.

Next, write a summary of what it says about Middle-of-funnel content
Response: Middle-of-funnel content includes brand-specific content that educates interested audiences that are already considering an offer.

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.
