Summarize the key points of the following passage about the history of summarization:

Summarization has been studied since the 1950s, shortly after the birth of information theory. Early approaches focused on extracting representative sentences from a text based on simple statistical and linguistic features. In the 1990s, machine learning techniques were applied to summarization, treating it as a classification problem where sentences are labeled as summary-worthy or not. Researchers also began exploring abstractive summarization, which involves generating new sentences that capture the main ideas of the source text. With the rise of deep learning in the 2010s, sequence-to-sequence models and attention mechanisms enabled significant progress in abstractive summarization. Large language models pre-trained on vast text corpora have further advanced the state of the art, allowing for more coherent and faithful summaries. However, challenges remain, such as dealing with long documents, maintaining factual consistency, and tailoring summaries to specific user needs.
Summarize the key points of the following passage about the history of summarization:

Summarization has been studied since the 1950s, shortly after the birth of information theory. Early approaches focused on extracting representative sentences from a text based on simple statistical and linguistic features. In the 1990s, machine learning techniques were applied to summarization, treating it as a classification problem where sentences are labeled as summary-worthy or not. Researchers also began exploring abstractive summarization, which involves generating new sentences that capture the main ideas of the source text. With the rise of deep learning in the 2010s, sequence-to-sequence models and attention mechanisms enabled significant progress in abstractive summarization. Large language models pre-trained on vast text corpora have further advanced the state of the art, allowing for more coherent and faithful summaries. However, challenges remain, such as dealing with long documents, maintaining factual consistency, and tailoring summaries to specific user needs.
user
The study of summarization began in the 1950s with extractive methods using statistical and linguistic features. Machine learning techniques were applied in the 1990s, treating it as a classification task. Abstractive summarization, generating new sentences, emerged later. Deep learning models with attention mechanisms enabled significant progress in abstractive summarization in the 2010s. Large pre-trained language models have further improved the quality and coherence of summaries, but challenges remain, such as handling long documents, ensuring factual accuracy, and adapting summaries to user requirements.
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 sum up what these former survivor players said about meeting Jeff Probst in a few sentences?

"Survivor: Island of the Idols" player Elaine Stott said that although she was nervous before she met the "Survivor" host, the fact that she was sitting in front of producers and Probst "didn't really matter to me." 

"I told dirty jokes, I flirted, I cussed, I made fun of myself. That's just how I am in general," Stott told Insider. "... I said, 'Jeff, you're just a man, you put your britches on just like I do.'" 

Freberg added that the interviewers just "tell you to start talking" instead of asking direct questions, and Probst sits there "trying to size you up and trying to be intimidating."

"You just need something funny to say, so I made it up on the spot," Freberg recalled. "... I look at Jeff and I go, 'My grandma's gonna freak the f--- out when she finds out I met you.'"

Freberg added that his joke "got a big laugh," so he was "off to a good start after that."
Response: When Elaine Stott was in front of Probst and producers her nervousness went away. Freberg said the producers didn't ask questions and he was told to just start talking. Jeff sized them up, but Freeberg came up with a funny joke from the start and that put them on good terms for the rest of the interview.

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.
