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.

Summarize the following text discussing the challenges and limitations of automatic summarization systems:

While automatic summarization systems offer significant advantages in terms of efficiency and speed, they also face several challenges and limitations. One of the primary challenges is accurately capturing the context and nuances of the original text, as machines may struggle to fully comprehend the underlying meaning and implications. Additionally, automatic summaries often lack coherence and flow, as the systems may struggle to logically organize and connect the extracted information. Another limitation is the inability to summarize highly technical or domain-specific content, which requires a deep understanding of the subject matter. Furthermore, automatic summaries may inadvertently introduce bias or misrepresent the original content due to the inherent limitations of the algorithms used. As a result, human oversight and validation are often necessary to ensure the accuracy and quality of the generated summaries, particularly in critical domains such as legal or medical contexts. Despite these challenges, ongoing research and advancements in natural language processing and machine learning aim to improve the capabilities of automatic summarization systems, making them more context-aware, coherent, and reliable.
user
The given text discusses the challenges and limitations faced by automatic summarization systems, despite their advantages of efficiency and speed. One significant challenge is accurately capturing the context and nuances of the original text, as machines may struggle to fully comprehend the underlying meaning and implications. Automatic summaries often lack coherence and logical organization, as the systems may struggle to connect the extracted information coherently. Additionally, these systems may have difficulty summarizing highly technical or domain-specific content due to the lack of deep subject matter understanding. Furthermore, the algorithms used in automatic summarization can introduce bias or misrepresent the original content, necessitating human oversight and validation, especially in critical domains like legal or medical contexts. Despite these limitations, ongoing research aims to improve the capabilities of automatic summarization systems, making them more context-aware, coherent, and reliable.
assistant