Summarize the following text in one concise paragraph:

Automatic text summarization is the process of creating a concise and precise summary of a longer text document. It aims to capture the most important information from the source text and present it in a condensed form. This task is challenging because it requires understanding the meaning and context of the text, identifying the key points, and generating a coherent and meaningful summary. Extractive summarization methods select and copy the most salient sentences or phrases from the original text, while abstractive techniques interpret the semantic content and generate entirely new sentences to form the summary. Summarization systems typically utilize natural language processing techniques, such as tokenization, part-of-speech tagging, named entity recognition, and semantic analysis, to analyze the text and determine the relevance and importance of different parts. The resulting summaries can be useful for quickly grasping the essence of lengthy documents, enabling efficient information access and consumption.
Summarize the following text in one concise paragraph:

Automatic text summarization is the process of creating a concise and precise summary of a longer text document. It aims to capture the most important information from the source text and present it in a condensed form. This task is challenging because it requires understanding the meaning and context of the text, identifying the key points, and generating a coherent and meaningful summary. Extractive summarization methods select and copy the most salient sentences or phrases from the original text, while abstractive techniques interpret the semantic content and generate entirely new sentences to form the summary. Summarization systems typically utilize natural language processing techniques, such as tokenization, part-of-speech tagging, named entity recognition, and semantic analysis, to analyze the text and determine the relevance and importance of different parts. The resulting summaries can be useful for quickly grasping the essence of lengthy documents, enabling efficient information access and consumption.
user
Automatic text summarization is the process of creating a concise summary of a longer text by capturing the most important information. It involves understanding the meaning, identifying key points, and generating a coherent summary. Extractive methods copy salient sentences, while abstractive techniques generate new sentences. Natural language processing techniques are used to analyze the text and determine relevance. The resulting summaries enable quick understanding of lengthy documents.
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.
