Summarize the key points about evaluation metrics for text summarization from the following text:

Evaluating the quality and effectiveness of automatic text summarization systems is a crucial task, and various metrics have been developed to assess different aspects of the generated summaries. Some commonly used evaluation metrics include ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap of n-grams between the generated summary and human-written reference summaries. BLEU (Bilingual Evaluation Understudy) is another metric borrowed from machine translation evaluation, which also measures n-gram overlap but with a focus on precision. These metrics are primarily used for evaluating extractive summaries. For abstractive summaries, metrics such as METEOR (Metric for Evaluation of Translation with Explicit ORdering) and BERTScore, which incorporate semantic similarity and contextual embeddings, are often employed. Additionally, human evaluation is considered the gold standard, where human annotators assess the summaries based on criteria such as informativeness, coherence, and fluency.
Summarize the key points about evaluation metrics for text summarization from the following text:

Evaluating the quality and effectiveness of automatic text summarization systems is a crucial task, and various metrics have been developed to assess different aspects of the generated summaries. Some commonly used evaluation metrics include ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap of n-grams between the generated summary and human-written reference summaries. BLEU (Bilingual Evaluation Understudy) is another metric borrowed from machine translation evaluation, which also measures n-gram overlap but with a focus on precision. These metrics are primarily used for evaluating extractive summaries. For abstractive summaries, metrics such as METEOR (Metric for Evaluation of Translation with Explicit ORdering) and BERTScore, which incorporate semantic similarity and contextual embeddings, are often employed. Additionally, human evaluation is considered the gold standard, where human annotators assess the summaries based on criteria such as informativeness, coherence, and fluency.
user
Evaluating text summarization systems is crucial, and various metrics have been developed. ROUGE and BLEU measure n-gram overlap between generated and reference summaries, primarily used for extractive summaries. METEOR and BERTScore incorporate semantic similarity and contextual embeddings, often used for abstractive summaries. Human evaluation, assessing informativeness, coherence, and fluency, is considered the gold standard. Different metrics assess different aspects of the generated summaries.
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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 read this text: 

People consume information in lots of different ways: visually, audibly, through text, and sometimes all three at once. Search engine algorithms like Google’s are advancing to reflect this experience for users. No longer will the algorithms just look at web pages as a single piece of text content. Google will look at all the ways real people consume information: text, images, videos, infographics, tables, charts, and interactive elements that make up a piece of content.

In recent years, we saw the rise of visual search – particularly in image-heavy industries like travel and tourism or ecommerce. Moving into 2023, Google will expand its foothold in multisearch and multisearch near me. 

What does it say about multi-format content in a brief summary?
Response: The text says that people prefer content in different forms like audio, video, or text format. As a result, Google plans to advance the multi-search function and page search algorithms accordingly. 

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.
