Please read the following text and provide a concise summary of the key points related to text summarization:

Automated text summarization is the process of producing a concise and comprehensive summary of one or more text documents, curated in a manner to convey the key information in the original text. Text summarization methods are widely used to digest lengthy text materials and quickly understand their essence. There are two main categories of text summarization: extractive and abstractive summarization. Extractive summarization selects the most relevant phrases, sentences, or passages from the original text and combines them to form summaries. Abstractive summarization, on the other hand, generates entirely new sentences to capture the main ideas of the text. While extractive methods are more commonly used due to their simplicity, abstractive methods can produce more coherent and concise summaries. Recent advancements in natural language processing and deep learning have significantly improved the quality of automated text summarization systems.
Please read the following text and provide a concise summary of the key points related to text summarization:

Automated text summarization is the process of producing a concise and comprehensive summary of one or more text documents, curated in a manner to convey the key information in the original text. Text summarization methods are widely used to digest lengthy text materials and quickly understand their essence. There are two main categories of text summarization: extractive and abstractive summarization. Extractive summarization selects the most relevant phrases, sentences, or passages from the original text and combines them to form summaries. Abstractive summarization, on the other hand, generates entirely new sentences to capture the main ideas of the text. While extractive methods are more commonly used due to their simplicity, abstractive methods can produce more coherent and concise summaries. Recent advancements in natural language processing and deep learning have significantly improved the quality of automated text summarization systems.
user
The given text explains automated text summarization, which is the process of generating concise summaries from lengthy text documents. It outlines two main approaches: extractive summarization, which selects important sentences from the original text, and abstractive summarization, which generates new sentences capturing the key ideas. While extractive methods are simpler, recent advancements in deep learning have improved the quality of abstractive summarization systems, enabling them to produce more coherent and concise summaries.
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.
