Summarize the following text, which explains the evaluation metrics commonly used to assess the quality of text summaries:

Evaluating the quality of automatically generated text summaries is crucial for measuring the effectiveness of summarization systems and identifying areas for improvement. Several evaluation metrics have been developed to assess summaries from different perspectives. Here are some commonly used metrics:

1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation): ROUGE is a widely adopted metric that measures the overlap between machine-generated summaries and reference (human-written) summaries. It calculates different variations of recall, such as ROUGE-N (N-gram overlap), ROUGE-L (Longest Common Subsequence), and ROUGE-SU (Skip-bigram plus unigram).

2. BLEU (Bilingual Evaluation Understudy): Originally developed for machine translation, BLEU measures the n-gram overlap between the generated summary and reference summaries, while also accounting for brevity to penalize short summaries.

3. METEOR (Metric for Evaluation of Translation with Explicit ORdering): METEOR computes a score based on the harmonic mean of unigram precision and recall, extended to consider stem, synonym, and paraphrase matching.

4. BERTScore: BERTScore leverages pre-trained language models like BERT to compute a contextualized similarity score between the generated summary and reference summaries, capturing semantic similarities beyond surface-level word overlap.

5. Pyramid: Pyramid is a manual evaluation method that involves creating a pyramid of summarization content units (SCUs) from reference summaries and scoring machine summaries based on their SCU coverage.

6. Human Evaluation: While automatic metrics provide quantitative assessments, human evaluation by expert annotators remains the gold standard for evaluating summary quality, coherence, fluency, and factual accuracy.

Evaluating summaries is a complex task, and different metrics capture different aspects of quality. It is common to report multiple metrics and combine them with human evaluation to gain a comprehensive understanding of a summarization system's performance.
Summarize the following text, which explains the evaluation metrics commonly used to assess the quality of text summaries:

Evaluating the quality of automatically generated text summaries is crucial for measuring the effectiveness of summarization systems and identifying areas for improvement. Several evaluation metrics have been developed to assess summaries from different perspectives. Here are some commonly used metrics:

1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation): ROUGE is a widely adopted metric that measures the overlap between machine-generated summaries and reference (human-written) summaries. It calculates different variations of recall, such as ROUGE-N (N-gram overlap), ROUGE-L (Longest Common Subsequence), and ROUGE-SU (Skip-bigram plus unigram).

2. BLEU (Bilingual Evaluation Understudy): Originally developed for machine translation, BLEU measures the n-gram overlap between the generated summary and reference summaries, while also accounting for brevity to penalize short summaries.

3. METEOR (Metric for Evaluation of Translation with Explicit ORdering): METEOR computes a score based on the harmonic mean of unigram precision and recall, extended to consider stem, synonym, and paraphrase matching.

4. BERTScore: BERTScore leverages pre-trained language models like BERT to compute a contextualized similarity score between the generated summary and reference summaries, capturing semantic similarities beyond surface-level word overlap.

5. Pyramid: Pyramid is a manual evaluation method that involves creating a pyramid of summarization content units (SCUs) from reference summaries and scoring machine summaries based on their SCU coverage.

6. Human Evaluation: While automatic metrics provide quantitative assessments, human evaluation by expert annotators remains the gold standard for evaluating summary quality, coherence, fluency, and factual accuracy.

Evaluating summaries is a complex task, and different metrics capture different aspects of quality. It is common to report multiple metrics and combine them with human evaluation to gain a comprehensive understanding of a summarization system's performance.
user
Evaluating the quality of text summaries involves various metrics that measure the overlap, similarity, or content coverage between machine-generated summaries and reference (human-written) summaries. Common metrics include ROUGE (recall-based), BLEU (n-gram precision), METEOR (stemming and paraphrasing), BERTScore (contextualized similarity), and Pyramid (manual content unit scoring). Human evaluation by expert annotators is considered the gold standard for assessing summary quality, coherence, fluency, and factual accuracy. Multiple metrics and human evaluation are often combined for a comprehensive evaluation.
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: Tell me, briefly, what this book description says about Native American women.

In the summer of 2017, twenty-two-year-old Savanna LaFontaine-Greywind vanished. A week after she disappeared, police arrested the white couple who lived upstairs from Savanna and emerged from their apartment carrying an infant girl. The baby was Savanna’s, but Savanna’s body would not be found for days.

The horrifying crime sent shock waves far beyond Fargo, North Dakota, where it occurred, and helped expose the sexual and physical violence Native American women and girls have endured since the country’s colonization.

With pathos and compassion, Searching for Savanna confronts this history of dehumanization toward Indigenous women and the government’s complicity in the crisis. Featuring in-depth interviews, personal accounts, and trial analysis, Searching for Savanna investigates these injustices and the decades-long struggle by Native American advocates for meaningful change.
Response: Native American women have been subjected to sexual and physical violence and dehumanization since the country's colonization.

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.
