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.

Text summarization is the process of generating a concise and accurate summary of a longer text document. Please summarize the following text and highlight the key points:

Text summarization is an important task that helps condense large volumes of information into concise and informative summaries. It has applications in various domains, including news articles, research papers, legal documents, and customer reviews. The goal of summarization is to capture the most salient information from the source text while preserving its overall meaning and context. There are two main approaches to text summarization: extractive and abstractive. Extractive summarization involves selecting and concatenating the most important sentences or phrases from the original text to form the summary. Abstractive summarization, on the other hand, generates entirely new sentences that capture the main ideas of the text in a concise and coherent manner. This approach often requires more advanced natural language processing techniques, such as semantic understanding and language generation. Recent advancements in machine learning, particularly in the field of deep learning, have led to significant improvements in the performance of abstractive summarization systems. However, extractive methods are still widely used due to their simplicity and efficiency.
user
The given text explains the concept of text summarization, which is the process of generating a concise summary of a longer text document. It highlights the key points: 1) Text summarization helps condense large volumes of information into concise and informative summaries. 2) It has applications in various domains like news articles, research papers, legal documents, and customer reviews. 3) There are two main approaches: extractive summarization (selecting and concatenating important sentences/phrases) and abstractive summarization (generating new sentences to capture main ideas). 4) Abstractive summarization requires advanced natural language processing techniques like semantic understanding and language generation. 5) Recent advancements in deep learning have improved abstractive summarization systems, but extractive methods are still widely used due to their simplicity and efficiency.
assistant