Summarize the key points discussed in the following passage on the role of deep learning in automatic text summarization:

Deep learning has revolutionized the field of automatic text summarization, enabling significant advancements in the quality and capabilities of summarization systems. Traditional methods, such as extractive summarization based on statistical features and rule-based approaches, have been largely surpassed by deep learning models, particularly those based on transformer architectures like BERT and GPT. These models leverage large pre-trained language models and self-attention mechanisms to capture long-range dependencies and contextual information in text, leading to more coherent and accurate summaries. Additionally, deep learning has enabled the development of powerful abstractive summarization techniques, where the model generates novel sentences that capture the essence of the input text, rather than simply extracting and rearranging existing sentences. This ability to produce fluent and concise summaries has opened up new possibilities for applications such as document summarization, headline generation, and text simplification. However, despite these advancements, challenges remain, including the need for large-scale annotated datasets, addressing factual inconsistencies and hallucinations, and improving the interpretability and robustness of deep learning models for summarization. Furthermore, the computational resources and energy requirements of training these models raise concerns about their environmental impact and scalability.
Summarize the key points discussed in the following passage on the role of deep learning in automatic text summarization:

Deep learning has revolutionized the field of automatic text summarization, enabling significant advancements in the quality and capabilities of summarization systems. Traditional methods, such as extractive summarization based on statistical features and rule-based approaches, have been largely surpassed by deep learning models, particularly those based on transformer architectures like BERT and GPT. These models leverage large pre-trained language models and self-attention mechanisms to capture long-range dependencies and contextual information in text, leading to more coherent and accurate summaries. Additionally, deep learning has enabled the development of powerful abstractive summarization techniques, where the model generates novel sentences that capture the essence of the input text, rather than simply extracting and rearranging existing sentences. This ability to produce fluent and concise summaries has opened up new possibilities for applications such as document summarization, headline generation, and text simplification. However, despite these advancements, challenges remain, including the need for large-scale annotated datasets, addressing factual inconsistencies and hallucinations, and improving the interpretability and robustness of deep learning models for summarization. Furthermore, the computational resources and energy requirements of training these models raise concerns about their environmental impact and scalability.
user
Deep learning, particularly transformer-based models like BERT and GPT, has revolutionized automatic text summarization by enabling more coherent and accurate summaries through capturing long-range dependencies and contextual information. It has facilitated powerful abstractive summarization techniques for generating novel, fluent summaries. However, challenges remain, including the need for large datasets, addressing factual inconsistencies, improving interpretability and robustness, and addressing environmental concerns related to computational resources and energy 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: Tell me the latest development in the US oil industry according to the following article.

In March, the Biden administration authorized a huge area of the Gulf of Mexico to be auctioned to oil companies for oil and gas drilling, the Guardian reports. The parcel is called lease 259, according to the Guardian. It stretches from the southern tip of Texas to the Alabama-Florida border, covering 73.3 million acres, an area as large as Italy. The first 1.6 million acres were put up for auction in March. Thirty-two oil companies bid a total of $309.7 million for the license to drill in the region at the Department of the Interior’s auction, the Guardian reports. It is expected that the companies who win the rights to lease 259 will drill in the area for the next 50 years, producing 1 billion barrels of oil and 4.4 trillion cubic feet of gas, the Guardian says. According to the Biden administration, this auction is required by the terms of the recent Inflation Reduction Act, the Guardian reports. While the bill made strides in shifting the U.S. to more affordable and cleaner sources of energy, it included compromises with the oil industry. However, according to the Guardian, while Biden was required to allow some drilling in the Gulf of Mexico, he was not forced to sign over such a huge area. “If this continues, all of the good Biden has done for the future will be undone by Biden himself,” Ben Jealous, executive director of the Sierra Club, told the Guardian.
Response: The Biden administration authorized the auction of lease 259 to oil companies for oil and gas drilling in March. Lease 259 is an Italy-sized portion of the Gulf of Mexico spanning from the southern tip of Texas to the Alabama-Florida border, which is expected to supply the company with the winning bid access to 1 billion barrels of oil and 4.4 trillion cubic feet of gas over the next 50 years. This move comes as part of a compromise made to pass Biden's recent Inflation Reduction Act, which otherwise is meant to shift the US to more affordable and cleaner sources of energy, and has been met with criticism from environmentalists.

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.
