Summarize the key points from the following paragraph about summarization evaluation metrics:

Evaluating the quality of automatically generated summaries is a critical challenge in text summarization. Several metrics have been proposed to assess the accuracy, fluency, and informativeness of summaries. The most widely used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap of n-grams between the generated summary and reference human-written summaries. While ROUGE is easy to compute and correlates well with human judgments, it has limitations, such as favoring extractive over abstractive summaries and failing to capture semantic equivalence. Recent work has focused on developing more advanced evaluation metrics, including BERTScore, which uses pre-trained language models to measure semantic similarity, and question-answering-based metrics that assess how well the summary answers questions about the input text. Human evaluation remains the gold standard, but is time-consuming and expensive.
Summarize the key points from the following paragraph about summarization evaluation metrics:

Evaluating the quality of automatically generated summaries is a critical challenge in text summarization. Several metrics have been proposed to assess the accuracy, fluency, and informativeness of summaries. The most widely used metric is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap of n-grams between the generated summary and reference human-written summaries. While ROUGE is easy to compute and correlates well with human judgments, it has limitations, such as favoring extractive over abstractive summaries and failing to capture semantic equivalence. Recent work has focused on developing more advanced evaluation metrics, including BERTScore, which uses pre-trained language models to measure semantic similarity, and question-answering-based metrics that assess how well the summary answers questions about the input text. Human evaluation remains the gold standard, but is time-consuming and expensive.
user
The paragraph discusses metrics for evaluating the quality of automatically generated text summaries. The most widely used metric is ROUGE, which measures n-gram overlap with human references but has limitations like favoring extractive summaries. Recent work has focused on more advanced metrics like BERTScore for semantic similarity and question-answering-based metrics. While human evaluation remains the gold standard, it is time-consuming and expensive. Developing effective automatic evaluation metrics is a critical challenge in summarization.
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: 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.
