Read the following passage on text summarization evaluation metrics and summarize the key points:

Evaluating the quality and effectiveness of text summarization systems is a crucial aspect of their development and deployment. Several metrics have been proposed to assess the performance of summarization algorithms, each with its own strengths and limitations.

One widely used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and reference summaries created by humans. ROUGE calculates different variants, such as ROUGE-N (measuring n-gram overlap) and ROUGE-L (measuring longest common subsequence). Higher ROUGE scores generally indicate better summarization performance, but ROUGE has limitations, such as not capturing semantic equivalence or coherence.

Another metric is BERTScore, which leverages pre-trained language models like BERT to evaluate the semantic similarity between the generated summary and reference summaries. BERTScore aims to overcome some of ROUGE's limitations by considering contextual embeddings and semantic similarity beyond surface-level n-gram matching.

Quality evaluation can also involve human judgments, where human annotators assess the summaries based on criteria like informativeness, coherence, and fluency. While subjective, human evaluations provide valuable insights into the summarization quality that automated metrics may miss.

Additionally, task-specific evaluation metrics can be employed based on the intended use case of the summarization system, such as measuring the impact on downstream tasks like question answering or information retrieval.
Read the following passage on text summarization evaluation metrics and summarize the key points:

Evaluating the quality and effectiveness of text summarization systems is a crucial aspect of their development and deployment. Several metrics have been proposed to assess the performance of summarization algorithms, each with its own strengths and limitations.

One widely used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and reference summaries created by humans. ROUGE calculates different variants, such as ROUGE-N (measuring n-gram overlap) and ROUGE-L (measuring longest common subsequence). Higher ROUGE scores generally indicate better summarization performance, but ROUGE has limitations, such as not capturing semantic equivalence or coherence.

Another metric is BERTScore, which leverages pre-trained language models like BERT to evaluate the semantic similarity between the generated summary and reference summaries. BERTScore aims to overcome some of ROUGE's limitations by considering contextual embeddings and semantic similarity beyond surface-level n-gram matching.

Quality evaluation can also involve human judgments, where human annotators assess the summaries based on criteria like informativeness, coherence, and fluency. While subjective, human evaluations provide valuable insights into the summarization quality that automated metrics may miss.

Additionally, task-specific evaluation metrics can be employed based on the intended use case of the summarization system, such as measuring the impact on downstream tasks like question answering or information retrieval.
user
The given passage discusses various metrics used for evaluating the quality and effectiveness of text summarization systems. Key points include:

1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a widely used metric that measures the overlap between generated summaries and human-created reference summaries, including variations like ROUGE-N (n-gram overlap) and ROUGE-L (longest common subsequence).

2. BERTScore leverages pre-trained language models like BERT to evaluate semantic similarity between generated and reference summaries, aiming to overcome ROUGE's limitations in capturing semantic equivalence and coherence.

3. Human judgments, while subjective, provide valuable insights into summarization quality by assessing criteria like informativeness, coherence, and fluency, which automated metrics may miss.

4. Task-specific evaluation metrics can be employed based on the intended use case of the summarization system, such as measuring the impact on downstream tasks like question answering or information retrieval.

The passage highlights the importance of using appropriate evaluation metrics, considering both automated measures and human judgments, to comprehensively assess the performance of text summarization algorithms.
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.
