Summarize the following text:

Text summarization is a crucial technique in natural language processing (NLP) that aims to condense lengthy text documents into shorter, concise summaries while preserving the most important information. It is particularly valuable in today's information-rich environment, where the volume of textual data is constantly growing, making it challenging to quickly grasp the essence of large documents. Text summarization can be classified into two main types: extractive and abstractive.

Extractive summarization involves selecting and combining the most informative sentences or phrases from the original text to create a summary. This method relies on identifying and extracting the most salient parts of the text without modifying or generating new content.

Abstractive summarization, on the other hand, generates entirely new sentences to capture the essence of the original text. This approach involves understanding the text at a deeper level and rephrasing the key concepts and ideas in a concise and coherent manner. Abstractive summarization is more complex and requires advanced natural language generation techniques, but it can produce more fluent and readable summaries.

Both extractive and abstractive summarization techniques have their advantages and challenges. Extractive methods are generally faster and easier to implement, but they may produce less coherent summaries or miss important details that require rephrasing. Abstractive methods can generate more natural and coherent summaries but are computationally more expensive and may introduce inaccuracies or biases.
Summarize the following text:

Text summarization is a crucial technique in natural language processing (NLP) that aims to condense lengthy text documents into shorter, concise summaries while preserving the most important information. It is particularly valuable in today's information-rich environment, where the volume of textual data is constantly growing, making it challenging to quickly grasp the essence of large documents. Text summarization can be classified into two main types: extractive and abstractive.

Extractive summarization involves selecting and combining the most informative sentences or phrases from the original text to create a summary. This method relies on identifying and extracting the most salient parts of the text without modifying or generating new content.

Abstractive summarization, on the other hand, generates entirely new sentences to capture the essence of the original text. This approach involves understanding the text at a deeper level and rephrasing the key concepts and ideas in a concise and coherent manner. Abstractive summarization is more complex and requires advanced natural language generation techniques, but it can produce more fluent and readable summaries.

Both extractive and abstractive summarization techniques have their advantages and challenges. Extractive methods are generally faster and easier to implement, but they may produce less coherent summaries or miss important details that require rephrasing. Abstractive methods can generate more natural and coherent summaries but are computationally more expensive and may introduce inaccuracies or biases.
user
Text summarization is a technique in natural language processing that condenses lengthy texts into concise summaries while preserving key information. There are two main types: extractive (selecting salient sentences/phrases) and abstractive (generating new sentences). Extractive is simpler but less coherent, while abstractive is more natural but complex. Both have advantages and challenges in terms of implementation and accuracy.
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 give me a summed up version of the following:

Fox News reached a last-second settlement with Dominion Voting Systems on Tuesday as the case raced toward opening statements, paying more than $787 million to end a colossal two-year legal battle that publicly shredded the right-wing network’s credibility.

Fox News’ $787.5 million settlement with Dominion Voting Systems is the largest publicly known defamation settlement in US history involving a media company.

The deal was announced hours after the jury was sworn in at the Delaware Superior Court. Rumors of a settlement swirled in the courthouse when, after a lunch break, the proceedings dramatically ground to a halt for nearly three hours with no explanation, while the parties apparently hammered out an accord.
Response: Fox News settled with Dominion Voting Systems and paid more than $787 million after two years of legal battles for a defamation suit. Just before the settlement, the jury had already been sworn in, and there were three hours of no information being shared while the settlement was made.

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.
