Summarize the following text about text summarization techniques:

Text summarization refers to the process of condensing a source text into a shortened version while retaining its essential information and overall meaning. There are two primary approaches to text summarization: extractive and abstractive.

Extractive summarization involves identifying and extracting the most salient sentences or fragments from the original text and combining them into a concise summary. This approach preserves the original wording and phrasing but may lack coherence due to the disjointed nature of the extracted fragments.

Abstractive summarization, on the other hand, aims to generate entirely new sentences that capture the core concepts and ideas of the source text. This approach requires a deeper understanding of the text's semantics and employs advanced natural language processing techniques, such as information extraction, sentence fusion, and language generation. Abstractive summaries tend to be more coherent and concise, but they may inadvertently introduce factual errors or omit important details.

Both extractive and abstractive summarization techniques can be further categorized as supervised or unsupervised, depending on whether they rely on pre-existing summaries for training or operate solely based on the source text and algorithms. Supervised methods typically involve machine learning models trained on datasets of source texts and their corresponding reference summaries, while unsupervised methods rely on heuristics, statistical analysis, and graph-based approaches to identify and rank the most relevant information.
Summarize the following text about text summarization techniques:

Text summarization refers to the process of condensing a source text into a shortened version while retaining its essential information and overall meaning. There are two primary approaches to text summarization: extractive and abstractive.

Extractive summarization involves identifying and extracting the most salient sentences or fragments from the original text and combining them into a concise summary. This approach preserves the original wording and phrasing but may lack coherence due to the disjointed nature of the extracted fragments.

Abstractive summarization, on the other hand, aims to generate entirely new sentences that capture the core concepts and ideas of the source text. This approach requires a deeper understanding of the text's semantics and employs advanced natural language processing techniques, such as information extraction, sentence fusion, and language generation. Abstractive summaries tend to be more coherent and concise, but they may inadvertently introduce factual errors or omit important details.

Both extractive and abstractive summarization techniques can be further categorized as supervised or unsupervised, depending on whether they rely on pre-existing summaries for training or operate solely based on the source text and algorithms. Supervised methods typically involve machine learning models trained on datasets of source texts and their corresponding reference summaries, while unsupervised methods rely on heuristics, statistical analysis, and graph-based approaches to identify and rank the most relevant information.
user
Text summarization involves condensing a source text into a shortened version while retaining essential information and meaning. Two main approaches are extractive summarization, which identifies and extracts key sentences or fragments from the original text, and abstractive summarization, which generates entirely new sentences capturing the core concepts. Both approaches can be supervised (using pre-existing summaries for training) or unsupervised (relying on algorithms and heuristics).
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: In short, what are Iron Man's powers? Draw your answer from the text below:

Without his suit of armor, Tony Stark has no supernatural powers. He is only limited by his imagination. Tony is a brilliant engineer and has used his talents to create a powerful suit of armor that enables the wearer to fly, shoot beams of energy from his hands and chest, and resist the vacuum of space. The suit also protects the wearer from damage and grants superhuman strength.

The suit is constantly being redesigned to face the new challenges that Tony Stark meets on a daily basis. There are specialty suits that have been made such as the Arctic, Stealth, Space, Hulkbuster, and Thorbuster armors. There have been nearly 40 different variations of the Iron Man armor in the current reality of the Iron Man comics.
Response: In short, Iron Man has no real superpowers outside his suit. Instead, he uses his engineering skillset to create armor that grants extreme strength, flight and energy beams. His suit also protects him from damage and he regularly upgrades it to meet his needs.

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.
