Summarize the following text about the history of summarization:

Summarizing goes back thousands of years, with early examples including the Alexandrian scholars who produced summaries of long works to help readers quickly determine a book's contents. In the 20th century, summarization techniques started being applied to technical literature as the output of scientific research grew exponentially. In 1958, H.P. Luhn wrote a paper describing automatically generating abstracts of documents by identifying linguistically-motivated features such as sentence length, word frequency, and word distribution. This work established the foundational concepts and algorithmic methods for automatic text summarization.

During the 1960s through 1990s, researchers focused on improving and refining summarization algorithms using rule-based and statistical approaches. For example, SUMMONS (1999) represented text using a vector space model and identified important sentences based on statistical features like word/phrase frequency. In the 2000s, summarization incorporated advances from other areas of NLP like machine learning models and semantic analysis.

More recently, neural networks and deep learning techniques have driven significant progress in abstractive summarization capabilities. Models like BART and T5 can produce fluent summaries of texts by representing the underlying semantics and generating novel content. However, these systems still struggle with aspects like factual consistency, coherence, and capturing contextual nuances.
Summarize the following text about the history of summarization:

Summarizing goes back thousands of years, with early examples including the Alexandrian scholars who produced summaries of long works to help readers quickly determine a book's contents. In the 20th century, summarization techniques started being applied to technical literature as the output of scientific research grew exponentially. In 1958, H.P. Luhn wrote a paper describing automatically generating abstracts of documents by identifying linguistically-motivated features such as sentence length, word frequency, and word distribution. This work established the foundational concepts and algorithmic methods for automatic text summarization.

During the 1960s through 1990s, researchers focused on improving and refining summarization algorithms using rule-based and statistical approaches. For example, SUMMONS (1999) represented text using a vector space model and identified important sentences based on statistical features like word/phrase frequency. In the 2000s, summarization incorporated advances from other areas of NLP like machine learning models and semantic analysis.

More recently, neural networks and deep learning techniques have driven significant progress in abstractive summarization capabilities. Models like BART and T5 can produce fluent summaries of texts by representing the underlying semantics and generating novel content. However, these systems still struggle with aspects like factual consistency, coherence, and capturing contextual nuances.
user
Text summarization dates back to ancient times, with early examples by scholars like the Alexandrians. Its modern foundations were established in the 1950s with algorithmic methods for automatic summarization. Over the decades, techniques improved through rule-based, statistical, and machine learning approaches. Currently, neural networks are driving advances in abstractive summarization, though challenges remain.
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: Condense the following piece of history into one or two sentences for me:

Before 1895, the area corresponding to modern-day South Gyeongsang Province was part of Gyeongsang Province, one of the Eight Provinces of Korea during the Joseon dynastic kingdom. In 1895, southern Gyeongsang was replaced by the districts of Jinju in the west and Dongnae (modern-day Busan) in the east. In 1896, they were merged to form South Gyeongsang Province.

The provincial capital was originally at Jinju; it moved in 1925 to Busan.During the Japanese rule of Korea, the province was known as Keishōnan-dō. In 1948, South Gyeongsang Province became part of South Korea. In 1963, Busan separated from South Gyeongsang Province to become a Directly Governed City (Jikhalsi). In 1983, the provincial capital moved from Busan to Changwon.
Response: Modern-day South Gyeongsang Province used to be part of Gyeongsang Province until 1895, with its current provincial capital being Changwon after changing twice. 

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.
