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

Extractive summarization is a relatively simpler approach to text summarization, where the most important sentences or phrases are selected from the original text and combined to form a summary. This technique relies on various statistical and linguistic features to identify and rank the relevance and importance of sentences. Common features used in extractive summarization include word frequency, sentence position, and semantic similarity measures.

In contrast, abstractive summarization is a more complex and challenging task that involves generating entirely new sentences to capture the essential meaning of the source text. This approach does not directly extract sentences or phrases from the original text but rather interprets and rephrases the content using advanced natural language processing techniques, such as deep learning models. Abstractive summarization aims to produce coherent, fluent summaries that may not necessarily contain any verbatim phrases from the original text.
user
The key differences between extractive and abstractive text summarization techniques, based on the given text, are: 1) Extractive summarization selects and combines important sentences or phrases directly from the original text, while abstractive summarization generates new sentences that capture the essence of the source text. 2) Extractive techniques rely on statistical and linguistic features to identify and rank the relevance of sentences, whereas abstractive techniques employ advanced natural language processing methods like deep learning models to interpret and rephrase the content. 3) Extractive summaries may contain verbatim phrases from the original text, while abstractive summaries aim to produce coherent, fluent summaries without necessarily including any verbatim phrases.
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