Summarize the following text in a few sentences: 

Automatic text summarization is a challenging task in natural language processing that aims to generate concise and informative summaries from lengthy source texts. It involves various subtasks, including content selection, information ordering, and linguistic generation. Traditional approaches to text summarization can be broadly categorized into extractive and abstractive methods. Extractive summarization techniques identify and extract the most salient sentences or phrases from the source text to form the summary. In contrast, abstractive summarization methods generate new sentences that capture the essential meaning of the source text, often requiring a deeper understanding of natural language and advanced generation capabilities. Recent advancements in deep learning and transformer models have significantly improved the performance of abstractive summarization systems, enabling the generation of more coherent and fluent summaries. However, challenges such as preserving factual consistency, handling low-resource domains, and accounting for context and background knowledge remain active areas of research.
Summarize the following text in a few sentences: 

Automatic text summarization is a challenging task in natural language processing that aims to generate concise and informative summaries from lengthy source texts. It involves various subtasks, including content selection, information ordering, and linguistic generation. Traditional approaches to text summarization can be broadly categorized into extractive and abstractive methods. Extractive summarization techniques identify and extract the most salient sentences or phrases from the source text to form the summary. In contrast, abstractive summarization methods generate new sentences that capture the essential meaning of the source text, often requiring a deeper understanding of natural language and advanced generation capabilities. Recent advancements in deep learning and transformer models have significantly improved the performance of abstractive summarization systems, enabling the generation of more coherent and fluent summaries. However, challenges such as preserving factual consistency, handling low-resource domains, and accounting for context and background knowledge remain active areas of research.
user
The given text discusses automatic text summarization, a Natural Language Processing task that generates concise summaries from lengthy texts. It covers the categories of extractive summarization (extracting salient sentences/phrases) and abstractive summarization (generating new sentences capturing the essence). The text highlights recent advancements using deep learning and transformer models for abstractive summarization, as well as ongoing challenges like factual consistency, low-resource domains, and incorporating context and background knowledge.
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: Can you summarize in one sentence what made Aonuma include the Ascend ability into TOTK?

Eiji Aonuma likes to cheat. “I’m somebody who, if I can find a way, likes to do that kind of gameplay,” the longtime Zelda developer told Polygon in an interview on Thursday, ahead of The Legend of Zelda: Tears of the Kingdom’s May 12 release. It’s an ethos from which Link’s new Ascend ability was formed — as a debug feature that Aonuma and director Hidemaro Fujibayashi used to easily exit the depths of Tears of the Kingdom’s caves.

“When I was exploring the caves, I would get to the destination where I was trying to get to, and once I checked it out, I would just use the debug code to get to the top,” Fujibayashi told Polygon. “And I thought, Well, maybe this is something that can be usable in the game.”

Aonuma agreed with Fujibayashi that it was a “pain” to go all the way back through Tears of the Kingdom’s labyrinthine caves. “When I heard that, I thought, Oh, I guess he would feel the same way that I would be feeling,” Fujibayashi said. “That’s how we got to implementing Ascend. To be blunt and honest, cheating can be fun. So that’s why we decided to drop it in there.”
Response: Aonuma used ascend as a debug feature initially to get out of the caves in the game and realized it would be useful for players to have the ability as well.

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.
