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:

Text summarization is a fundamental task in natural language processing that aims to create a concise and meaningful summary of a longer text document. This process involves identifying the key ideas, essential information, and critical details within the original text and presenting them in a condensed form.

There are two main approaches to text summarization: extractive and abstractive. Extractive summarization techniques select and combine the most relevant sentences or phrases from the original text to form the summary. These methods often rely on statistical features, such as word frequency, position, and the presence of keywords, to determine the importance of each sentence.

Abstractive summarization, on the other hand, involves generating entirely new sentences that capture the essential information from the input text. These techniques employ advanced natural language generation models, often based on deep learning architectures like sequence-to-sequence (seq2seq) models and transformer-based models like BERT and GPT. Abstractive summarization is generally considered more challenging than extractive summarization, as it requires a deeper understanding of the text and the ability to generate coherent and fluent natural language output.

Recent years have witnessed significant advancements in abstractive text summarization, driven by the success of large pre-trained language models and the availability of large-scale summarization datasets. These models can be fine-tuned on specific summarization tasks, achieving state-of-the-art performance and generating high-quality summaries that accurately capture the essence of the input text while maintaining coherence and fluency.
user
Text summarization is a fundamental natural language processing task that aims to create a concise and meaningful summary of a longer text document by identifying the key ideas, essential information, and critical details. There are two main approaches: extractive summarization (selecting and combining relevant sentences or phrases from the original text based on statistical features like word frequency and position) and abstractive summarization (generating new sentences capturing the essential information using advanced natural language generation models like sequence-to-sequence and transformer-based models). Abstractive summarization, considered more challenging due to its requirement for a deeper text understanding and coherent language generation, has seen significant advancements driven by large pre-trained language models and the availability of large-scale summarization datasets, achieving state-of-the-art performance in generating high-quality, accurate, and fluent summaries.
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