Read the following paragraph carefully and summarize the main points in a few sentences:

Automatic text summarization is a rapidly evolving field in natural language processing that focuses on generating concise and informative summaries from lengthy text documents. It aims to capture the most salient information and key points while minimizing redundancy and irrelevant details. Summarization algorithms can be broadly categorized into two main approaches: extractive and abstractive. Extractive methods identify and extract the most important sentences or phrases from the original text and concatenate them to form a summary. In contrast, abstractive methods employ advanced techniques, such as natural language generation and deep learning, to generate entirely new sentences that capture the essence of the text in a more coherent and fluent manner. Summarization finds applications in various domains, including news aggregation, academic research, legal document analysis, and customer service, enabling efficient information consumption and decision-making.
Read the following paragraph carefully and summarize the main points in a few sentences:

Automatic text summarization is a rapidly evolving field in natural language processing that focuses on generating concise and informative summaries from lengthy text documents. It aims to capture the most salient information and key points while minimizing redundancy and irrelevant details. Summarization algorithms can be broadly categorized into two main approaches: extractive and abstractive. Extractive methods identify and extract the most important sentences or phrases from the original text and concatenate them to form a summary. In contrast, abstractive methods employ advanced techniques, such as natural language generation and deep learning, to generate entirely new sentences that capture the essence of the text in a more coherent and fluent manner. Summarization finds applications in various domains, including news aggregation, academic research, legal document analysis, and customer service, enabling efficient information consumption and decision-making.
user
Automatic text summarization is a field in natural language processing that generates concise and informative summaries from lengthy text documents, capturing the most salient information and key points while minimizing redundancy and irrelevant details. Summarization algorithms can be categorized into extractive methods, which identify and extract important sentences or phrases from the original text, and abstractive methods, which employ advanced techniques like natural language generation and deep learning to generate new sentences capturing the text's essence coherently and fluently. Summarization finds applications in various domains, including news aggregation, academic research, legal document analysis, and customer service, enabling efficient information consumption and decision-making.
assistant
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: Hello. Please read over the information below and create a summary of the following:

The story is set around the turn of the twentieth century, during the British era of colonialism. It begins with Colonel Masters (John Williams, excellent as always portraying a classic stiff-upper-lip British gent) returning home after some time away. He is greeted by his housekeeper, Miss Danton (Shani Wallis) who asks how long he’ll be home to which he replies “that will depend on the whims of the general staff at the colonial office.”

His young niece Monica also lives here and she greets her uncle and excitedly tells him that she has a new doll, which she has with her. When Col. Masters’ eyes glance downward to the doll, his expression changes to one of shock and fear.  When we see the doll, the viewer’s face may do the same, even before hearing the doll’s back story.
Response: At the beginning of the 20th century, British colonial Colonel Masters returns home to his housekeeper, Miss Danton. Monica, his young niece, also lives at the house with Miss Danton and is happy to see her uncle. But when she shows him her new doll, his reaction changes to one of horror and alarm. 

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.
