Summarize the key points about evaluation metrics for summarization systems discussed in the following text:

Evaluating the quality of summarization systems is crucial for their development and improvement. Several metrics are commonly used to assess the performance of these systems:

1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation): A set of metrics that measure the overlap between the generated summary and reference summaries based on n-gram co-occurrence statistics. It includes ROUGE-N (N-gram overlap), ROUGE-L (longest common subsequence), and ROUGE-SU (skip-bigram plus unigram co-occurrence statistics).

2. BLEU (Bilingual Evaluation Understudy): Originally designed for machine translation evaluation, BLEU measures the n-gram overlap between the generated summary and reference summaries while considering brevity.

3. METEOR (Metric for Evaluation of Translation with Explicit Ordering): Evaluates summaries based on the harmonic mean of unigram precision and recall, considering stem, synonym, and paraphrase matches.

4. BERTScore: Computes a similarity score between the generated summary and reference summaries using contextual embeddings from pre-trained language models like BERT.

5. Human evaluation: While automatic metrics are useful for quick evaluation, human evaluation by experts or crowdsourcing is often considered the gold standard for assessing summary quality in terms of coherence, fluency, and information coverage.

It is common to use multiple evaluation metrics and combine them with human evaluation to gain a comprehensive understanding of a summarization system's performance.
Summarize the key points about evaluation metrics for summarization systems discussed in the following text:

Evaluating the quality of summarization systems is crucial for their development and improvement. Several metrics are commonly used to assess the performance of these systems:

1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation): A set of metrics that measure the overlap between the generated summary and reference summaries based on n-gram co-occurrence statistics. It includes ROUGE-N (N-gram overlap), ROUGE-L (longest common subsequence), and ROUGE-SU (skip-bigram plus unigram co-occurrence statistics).

2. BLEU (Bilingual Evaluation Understudy): Originally designed for machine translation evaluation, BLEU measures the n-gram overlap between the generated summary and reference summaries while considering brevity.

3. METEOR (Metric for Evaluation of Translation with Explicit Ordering): Evaluates summaries based on the harmonic mean of unigram precision and recall, considering stem, synonym, and paraphrase matches.

4. BERTScore: Computes a similarity score between the generated summary and reference summaries using contextual embeddings from pre-trained language models like BERT.

5. Human evaluation: While automatic metrics are useful for quick evaluation, human evaluation by experts or crowdsourcing is often considered the gold standard for assessing summary quality in terms of coherence, fluency, and information coverage.

It is common to use multiple evaluation metrics and combine them with human evaluation to gain a comprehensive understanding of a summarization system's performance.
user
The key points about evaluation metrics for summarization systems are: 1) ROUGE measures overlap between generated and reference summaries based on n-gram co-occurrence. 2) BLEU measures n-gram overlap while considering brevity. 3) METEOR evaluates based on unigram precision, recall, and matches like stems and synonyms. 4) BERTScore computes similarity using contextual embeddings from pre-trained language models. 5) Human evaluation by experts or crowdsourcing is considered the gold standard for assessing quality, coherence, fluency, and information coverage. Multiple automatic 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: Could you summarize what this article says are ways to use essential oils? The article says:
AROMA DIFFUSER
This method is beneficial for mood enhancement and upliftment.  It disinfects the air and can boost immunity through inhalation.  Fill the bowl with water, add 5-15 drops and enjoy
STEAM INHALATION
This method is used mainly for upper respiratory problems such as bronchial and sinus congestion, colds, coughs, and influenza.  Fill a large bowl with boiling water and add three drops of essential oil.  Lean over the bowl with your eyes closed and a towel over your head and inhale the steam for 5-10 minutes.
VAPORIZER
An effective decongesting method for respiratory conditions.  Add from 3 to 9 drops of essential oil to the water of the vaporizer.  Run overnight.
Response: Three ways to use essential oils include in an aroma diffuser, via steam inhalation, and in a vaporizer. 

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.
