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 key differences between extractive and abstractive text summarization based on the following text:

Extractive and abstractive text summarization are two distinct approaches to generating concise summaries from longer text documents. Extractive summarization involves selecting and concatenating the most important sentences or phrases from the original text to form the summary. This method relies on identifying and extracting the most relevant content without modifying the text itself. Extractive summarization algorithms typically use statistical or machine learning techniques to rank sentences based on features like word frequency, position, and similarity to the overall text. While extractive summaries can capture the key points, they may lack coherence and fluency since they are composed of extracted sentences without further processing. In contrast, abstractive summarization involves generating entirely new sentences that capture the essence of the text. This approach requires a deeper understanding of the text and the ability to paraphrase, rephrase, or compress the content. Abstractive summarization algorithms typically use advanced natural language processing and generation techniques, such as sequence-to-sequence models and attention mechanisms, to understand the text and generate coherent summaries. While abstractive summaries can be more concise and fluent, they are generally more complex and computationally expensive than extractive summaries.
user
The key differences between extractive and abstractive text summarization are: 1) Extractive summarization selects and concatenates important sentences or phrases from the original text, while abstractive summarization generates entirely new sentences to capture the essence of the text. 2) Extractive methods rely on identifying relevant content without modifying the text, using techniques like statistical analysis and machine learning, while abstractive methods require a deeper understanding of the text and the ability to paraphrase and rephrase the content. 3) Extractive summaries may lack coherence and fluency as they are composed of extracted sentences, while abstractive summaries can be more concise and fluent but are more complex and computationally expensive. 4) Extractive methods typically use simpler techniques, while abstractive methods employ advanced natural language processing and generation techniques like sequence-to-sequence models and attention mechanisms.
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