Summarize the main points from the following text about summarization techniques:

Text summarization is the process of creating a concise and precise summary of a longer text while retaining the key information and overall meaning. There are two main approaches to summarization: extraction and abstraction. Extractive summarization selects and concatenates the most important sentences from the original text to form the summary. Abstractive summarization, on the other hand, generates entirely new sentences to capture the essence of the original content. Advances in natural language processing and deep learning have enabled the development of powerful abstractive summarization models that can produce high-quality summaries on par with those written by humans. However, extractive methods are still widely used due to their simplicity, efficiency, and lower computational requirements. Key challenges in summarization include preserving context, avoiding redundancy, and ensuring coherence and fluency in the generated summaries.
Summarize the main points from the following text about summarization techniques:

Text summarization is the process of creating a concise and precise summary of a longer text while retaining the key information and overall meaning. There are two main approaches to summarization: extraction and abstraction. Extractive summarization selects and concatenates the most important sentences from the original text to form the summary. Abstractive summarization, on the other hand, generates entirely new sentences to capture the essence of the original content. Advances in natural language processing and deep learning have enabled the development of powerful abstractive summarization models that can produce high-quality summaries on par with those written by humans. However, extractive methods are still widely used due to their simplicity, efficiency, and lower computational requirements. Key challenges in summarization include preserving context, avoiding redundancy, and ensuring coherence and fluency in the generated summaries.
user
The text discusses two main approaches to text summarization: extraction and abstraction. Extractive summarization selects and combines key sentences from the original text, while abstractive summarization generates new sentences to capture the main ideas. Advancements in natural language processing and deep learning have enabled high-quality abstractive summarization models, though extractive methods remain popular due to their efficiency. Key challenges include preserving context, avoiding redundancy, and ensuring coherence in the 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.
