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

Evaluating the quality of automatically generated summaries is a crucial task in the field of text summarization. Several evaluation metrics have been proposed to assess the performance of summarization systems. These metrics can be broadly divided into two categories: reference-based and reference-free.

Reference-based metrics compare the generated summary with one or more human-written reference summaries. Popular metrics in this category include ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap of n-grams between the system and reference summaries, and BLEU (Bilingual Evaluation Understudy), which calculates the n-gram precision of the system summary against the references.

Reference-free metrics, on the other hand, evaluate the quality of the summary without relying on reference summaries. These metrics often assess properties like coherence, readability, and information coverage. Examples include PYRAMID, which evaluates the content selection and distribution in the summary, and Jensen-Shannon divergence, which measures the similarity between the word distributions of the summary and the source text.

Additionally, human evaluation is widely used to assess the quality of summaries from different perspectives, such as informativeness, coherence, and overall quality. However, human evaluation is time-consuming and expensive, making it challenging to use for large-scale evaluations.

The choice of evaluation metric depends on the specific requirements of the summarization task and the availability of reference summaries. A combination of different metrics is often used to provide a more comprehensive assessment of the summarization system's performance.
Summarize the key points discussed in the following text about different evaluation metrics used for text summarization systems:

Evaluating the quality of automatically generated summaries is a crucial task in the field of text summarization. Several evaluation metrics have been proposed to assess the performance of summarization systems. These metrics can be broadly divided into two categories: reference-based and reference-free.

Reference-based metrics compare the generated summary with one or more human-written reference summaries. Popular metrics in this category include ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap of n-grams between the system and reference summaries, and BLEU (Bilingual Evaluation Understudy), which calculates the n-gram precision of the system summary against the references.

Reference-free metrics, on the other hand, evaluate the quality of the summary without relying on reference summaries. These metrics often assess properties like coherence, readability, and information coverage. Examples include PYRAMID, which evaluates the content selection and distribution in the summary, and Jensen-Shannon divergence, which measures the similarity between the word distributions of the summary and the source text.

Additionally, human evaluation is widely used to assess the quality of summaries from different perspectives, such as informativeness, coherence, and overall quality. However, human evaluation is time-consuming and expensive, making it challenging to use for large-scale evaluations.

The choice of evaluation metric depends on the specific requirements of the summarization task and the availability of reference summaries. A combination of different metrics is often used to provide a more comprehensive assessment of the summarization system's performance.
user
The text discusses different evaluation metrics used to assess the quality of automatically generated summaries from text summarization systems. It categorizes these metrics into reference-based, which compare the system summary to human-written reference summaries (e.g., ROUGE, BLEU), and reference-free, which evaluate properties like coherence and information coverage without using references (e.g., PYRAMID, Jensen-Shannon divergence). The text also mentions human evaluation as a widely used but time-consuming method. The choice of metric depends on the task requirements and availability of references, with a combination of metrics often used for 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.
