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 is a brief summary of the following post?
Two species of palo santo trees are distributed in the Galápagos archipelago, the native Bursera graveolens, and the endemic Bursera malacophylla (Burseraceae). Native Bursera graveolens is the same variety you can find in mainland Ecuador and other countries like Peru. It is called native because it is not an introduced plant, but it came to the islands naturally, thousands of years ago. Bursera malacophylla, on the other hand, grows exclusively in the Galapagos Islands. Palo santo trees (Bursera Jacq. ex L.; Burseraceae) are among the most common and easily recognizable indicator species of the dry and transition zone flora in the Galápagos archipelago. Most of the year, the tree is leafless, but in the rainy season, it quickly produces thin blue-green leaves with toothed edges. The olive shapes fruit turn bright red when ripe, and burst open to reveal black seeds inside. Palo Santo trees (Bursera graveolens) lose their leaves during the dry season to help stop water loss.
Response: There are two types of palo santo tree in the Galápagos called the Bursera graveolens and Bursera malacophylla.

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 passage: Text summarization is a crucial task in the field of natural language processing (NLP) and information retrieval. It involves the process of distilling the most important information from a longer text or collection of texts into a condensed and coherent summary. The primary goal of text summarization is to present the key concepts, ideas, and insights in a concise and readable format, while preserving the overall meaning and context of the original content. This task is particularly valuable in scenarios where individuals or organizations need to quickly grasp the essence of large volumes of textual data, such as research papers, news articles, legal documents, or online content. Automated text summarization systems leverage various techniques, including statistical methods, machine learning algorithms, and deep learning models, to analyze and understand the structure, semantics, and context of the input text. These systems can generate summaries using either extractive methods, which involve selecting and concatenating the most relevant sentences or phrases from the original text, or abstractive methods, which aim to generate entirely new sentences that capture the underlying meaning and key concepts.
user
Text summarization is a crucial NLP and information retrieval task that distills the most important information from longer texts into condensed, coherent summaries. Its goal is to present key concepts, ideas, and insights concisely while preserving meaning and context, valuable for quickly grasping large textual data. Automated systems use statistical methods, machine learning, and deep learning to analyze text structure, semantics, and context, generating summaries through extractive (selecting relevant sentences/phrases) or abstractive (generating new sentences capturing underlying meaning) methods.
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