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 summarize:

TLS, short for Transport Layer Security, and SSL, short for Secure Socket Layers, are both cryptographic protocols that encrypt data and authenticate a connection when moving data on the Internet.

For example, if you’re processing credit card payments on your website, TLS and SSL can help you securely process that data so that malicious actors can’t get their hands on it.

So what’s the difference between TLS vs SSL?

Well, TLS is actually just a more recent version of SSL. It fixes some security vulnerabilities in the earlier SSL protocols.

Before you learn more about the specifics, it’s important to understand the basic history of SSL and TLS.

SSL 2.0 was first released in February 1995 (SSL 1.0 was never publicly released because of security flaws). Although SSL 2.0 was publicly released, it also contained security flaws and was quickly replaced by SSL 3.0 in 1996.

Then, in 1999, the first version of TLS (1.0) was released as an upgrade to SSL 3.0. Since then, there have been three more TLS releases, with the most recent release being TLS 1.3 in August 2018.
Response: TLS (Transport Layer Security) and SSL (Secure Socket Layers) are both cryptographic protocols that authenticate a connection and encrypt data on the Internet. If you're ver processed credit card payments on a website, you've used either TLS or SSL to do so to secure the process from bad actors. What's the difference between them? TLS is simply a more recent version of SSL, which fixes some security vulnerabilities that were in previous SSL protocols.

The basic history of SSL and TLS is as follows: SSL 2.0 was released in February 1995 (SSL 1.0 never made it to the public) Although SSL 2.0 had a public release, it contained security flaws as well and was replaced by SSL 3.0 in 1996. In 1999, TLS 1.0 was released as an upgrade to SSL 3.0. Three more TLS releases have since come. TLS 1.3 was released in August 2018.

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.

Please summarize the following text:

Summarization is a crucial task in natural language processing (NLP) that aims to condense lengthy texts into concise and informative summaries. This process involves identifying the most relevant information, discarding redundant or unnecessary details, and presenting the essential content in a coherent and readable manner. Summarization techniques can be broadly classified into two categories: extractive and abstractive. Extractive summarization involves selecting and combining the most important sentences or phrases from the original text to form the summary. This approach relies on scoring and ranking sentences based on features such as word frequency, position, and sentence length. Abstractive summarization, on the other hand, generates entirely new sentences that capture the essence of the text using advanced language generation models. This approach requires a deeper understanding of the text's semantics and the ability to paraphrase and synthesize information. Evaluation of summarization systems is a challenging task, as it involves assessing the quality, coherence, and informativeness of the generated summaries. Various evaluation metrics have been developed, such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between the generated summary and reference human-written summaries.
user
Summarization is a crucial NLP task that condenses lengthy texts into concise and informative summaries by identifying relevant information, discarding redundancies, and presenting essential content coherently. Techniques are classified as extractive (selecting and combining important sentences/phrases) or abstractive (generating new sentences capturing the essence using language models). Extractive methods score and rank sentences, while abstractive approaches require deeper semantic understanding and paraphrasing. Evaluation involves assessing quality, coherence, and informativeness against human-written summaries using metrics like ROUGE.
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