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: Summarize the main points in the text below - 

You'll always want to have access to sufficient emergency funds for, well, emergencies. Make sure you can afford a health emergency (some insurance plans require you to pay first and claim for reimbursement later), a one-way ticket home in the event that something urgent happens, a missed flight, or a theft or unexpected mishap. And, more practically, you may find that you've under-budgeted and you need to increase your budget a bit while you're on the road, so having access to some emergency cash might prevent you from having to cut your trip short or miss out on things you wanted to see.

In short, you need to be able to afford to get yourself out of bad situations if you really have to. Remember that as a solo traveler you will generally not have another person readily available to help with bad situations, so it's up to you to be able to manage them.

It's a good idea to have a variety of different sources of emergency funds, including cash in hard currencies like US dollars or Euros, a credit card (and backup) with a decent limit, and a trusted friend or relative back home who can send you money in an emergency.
Response: - Have money set aside for emergencies like health issues or an emergency trip home
- Budget enough to get yourself out of unexpected situations if needed
- Remember, if you’re by yourself you will be the one managing any problems
- Carry a few different ways to pay for things if needed like cash and credit cards, and  have someone to borrow from back home if needed

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 key points discussed in the following paragraph about text summarization techniques:

Text summarization is a long-standing challenge in natural language processing, with the goal of automatically generating concise and informative summaries that capture the essence of a source document. There are two primary approaches to text summarization: extractive and abstractive. Extractive summarization selects and concatenates the most salient sentences or text segments from the input, leveraging techniques like sentence scoring, graph-based ranking, and integer linear programming to identify the most representative content. This approach is relatively straightforward to implement but can sometimes produce incoherent or redundant summaries. Abstractive summarization, on the other hand, generates novel sentences that convey the key information from the source text, often utilizing advanced language generation models such as sequence-to-sequence architectures or graph transformations. While abstractive methods have the potential to produce more coherent and concise summaries, they are generally more computationally expensive and can struggle with factual inconsistencies or hallucinations. Regardless of the approach, evaluation of summarization systems remains an open challenge, with metrics like ROUGE, BLEU, and question-answering performance serving as imperfect but useful proxies for assessing summary quality. Current research explores techniques for improving context modeling, handling long-range dependencies, increasing factual consistency, and developing more robust evaluation protocols.
user
- Text summarization aims to generate concise, informative summaries capturing the essence of source documents
- Two main approaches: extractive (selecting and concatenating salient sentences/text spans) and abstractive (generating novel sentences)
- Extractive uses techniques like sentence scoring, graph ranking, integer programming to identify representative content
- Extractive is relatively straightforward but can produce incoherent or redundant summaries
- Abstractive leverages advanced language models (seq2seq, graph transformations) to generate new sentences conveying key information
- Abstractive has potential for more coherent, concise summaries but is computationally expensive and can have factual inconsistencies
- Evaluation uses imperfect proxies like ROUGE, BLEU, question-answering performance
- Research focuses on improving context modeling, long-range dependencies, factual consistency, robust evaluation protocols
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