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: Please summarize how Asian American and Pacific Islander Heritage Month was started based on the text below: 

History
A former congressional staffer in the 1970s, Jeanie Jew, first approached Representative Frank Horton with the idea of designating a month to recognize Asian Pacific Americans, following the bicentennial celebrations.[19] In June 1977, Representatives Horton, and Norman Y. Mineta, introduced a United States House of Representatives resolution to proclaim the first ten days of May as Asian-Pacific Heritage Week.[20][21] A similar bill was introduced in the Senate a month later by Daniel Inouye and Spark Matsunaga.[20][22]

The proposed resolutions sought that May be designated for two reasons. For on May 7, 1843, the first Japanese immigrant arrived in the United States.[23][24][25] More than two decades later, on May 10, 1869, the golden spike was driven into the first transcontinental railroad, which was completed using Chinese labor.[23][24][26]

President Jimmy Carter signed a joint resolution for the celebration on October 5, 1978.[20]

On May 1, 2009, President Barack Obama signed Proclamation 8369, recognizing the month of May as Asian American and Pacific Islander Heritage Month.[1][27]
Response: A former congressional staffer with the idea approached a representative about it in the 1970s. In 1977, a resolution was introduced in the House and the Senate, and the President signed it in 1978. However, that resolution was only for the first ten days of May. It wasn't until 2009 that the whole month of May was named Asian American and Pacific Islander Heritage Month.

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 key points about recent advancements and future directions in automatic summarization highlighted in the following text:

Automatic summarization has witnessed significant advancements in recent years, driven by the development of powerful deep learning models and the availability of large-scale datasets for training and evaluation.

One of the most notable advancements has been the application of transformer architectures, such as BERT and GPT, to summarization tasks. These models have shown remarkable performance in capturing the contextual and semantic information within text, enabling the generation of more coherent and fluent summaries.

Additionally, the use of pre-trained language models and transfer learning techniques has further boosted the performance of summarization systems, allowing them to leverage the knowledge acquired from large corpora during pre-training and fine-tune on specific summarization datasets.

Another significant development has been the exploration of abstractive summarization approaches, which generate new, concise text that captures the essence of the original content. While extractive methods have been traditionally more prevalent, abstractive techniques, powered by sequence-to-sequence models and attention mechanisms, have shown promising results in generating more natural and compact summaries.

Researchers are also exploring multi-document and multi-modal summarization, expanding beyond single-document text summarization to handle multiple sources of information, including images, videos, and audio data. This is particularly relevant in domains such as news, social media, and multimedia content, where information is often presented in various formats.

Looking ahead, future directions in automatic summarization include:

1. Improving factual consistency and mitigating bias in generated summaries.
2. Developing personalized and context-aware summarization systems that can adapt to different users, domains, and scenarios.
3. Exploring more efficient and scalable methods for summarizing large-scale and streaming data sources.
4. Enhancing the interpretability and explainability of summarization models, enabling better transparency and trust.
5. Addressing ethical considerations, such as privacy, fairness, and accountability, in the development and deployment of summarization systems.

As the field continues to evolve, automatic summarization is poised to play an increasingly important role in enabling efficient information access and knowledge discovery across various domains and applications.
user
Recent advancements in automatic summarization include the application of transformer models like BERT and GPT, leveraging pre-trained language models and transfer learning, and the exploration of abstractive summarization techniques. Multi-document and multi-modal summarization are also being explored. Future directions involve improving factual consistency and mitigating bias, developing personalized and context-aware systems, exploring efficient and scalable methods for large-scale data, enhancing interpretability and explainability, and addressing ethical considerations like privacy and fairness. These advancements and future directions aim to enable more efficient information access and knowledge discovery across various domains and applications through advanced summarization capabilities.
assistant