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: What does the following article say about the Federal Reserve?

The Federal Reserve reinforced its fight against high inflation Wednesday by raising its key interest rate by a quarter-point to the highest level in 16 years. But the Fed also signaled that it may now pause the streak of 10 rate hikes that have made borrowing for consumers and businesses steadily more expensive.

In a statement after its latest policy meeting, the Fed removed a sentence from its previous statement that had said “some additional” rate hikes might be needed. It replaced it with language that said it will consider a range of factors in “determining the extent” to which future hikes might be needed.

Speaking at a news conference, Chair Jerome Powell said the Fed has yet to decide whether to suspend its rate hikes. But he pointed to the change in the statement’s language as confirming at least that possibility.

Response: Interest rates were raised by the Federal Reserve to combat inflation. In the future, they will consider a range of factors before raising rates again. Jerome Powell the Chair of the Federal Reserve hinted at suspending rate hikes. 

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.

Summarize the following details about different types of summarization techniques and their applications:

Summarization techniques can be broadly categorized into two main types: extractive and abstractive. Extractive summarization involves identifying and selecting the most important sentences or phrases from the original text and concatenating them to form the summary. This approach is simpler and more straightforward, but it may produce less coherent summaries since the extracted sentences are not modified or adapted to fit together seamlessly.

On the other hand, abstractive summarization generates new sentences that capture the main ideas of the source text. This approach requires a deeper understanding of the text and the ability to paraphrase and restructure the content. Abstractive summaries tend to be more coherent and readable, but they are more challenging to generate and may introduce errors or inaccuracies if not done correctly.

Within these two broad categories, various specific techniques have been developed. For extractive summarization, popular methods include scoring and ranking sentences based on features like term frequency, position, and relevance to the main topics. Techniques like TextRank and LexRank use graph-based algorithms to identify and prioritize important sentences.

For abstractive summarization, sequence-to-sequence models and transformer architectures have shown promising results. These deep learning models can learn to generate summaries by encoding the source text and decoding it into a concise summary. Attention mechanisms and copy mechanisms have been incorporated to improve the accuracy and coherence of generated summaries.

Summarization techniques find applications in various domains, including news articles, scientific literature, legal documents, customer reviews, and social media data. In the news domain, summaries can provide quick overviews of current events, while in scientific literature, they can help researchers quickly grasp the key findings and contributions of a paper. Legal document summarization can assist in quickly understanding complex cases and rulings.

Customer review summarization is valuable for businesses and consumers, as it can distill the main sentiments and opinions from a large volume of reviews. Social media summarization can help identify trending topics, track public opinion, and monitor crisis situations.
user
The given text discusses different types of summarization techniques and their applications. It categorizes summarization into two main types: extractive and abstractive. Extractive summarization involves selecting and concatenating important sentences or phrases from the original text, while abstractive summarization generates new sentences that capture the main ideas. The text outlines specific techniques for each type, such as scoring and ranking sentences for extractive summarization, and using sequence-to-sequence models and transformer architectures for abstractive summarization. The applications of summarization techniques are mentioned, including news, scientific literature, legal documents, customer reviews, and social media data. In each domain, summarization can provide concise overviews, highlight key findings, distill opinions, and identify trending topics.
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