Summarize the following passage on the history of summarization techniques:

The origins of summarization can be traced back to the advent of automated abstracting in the late 1950s and 1960s. Early work focused on extracting key sentences from text and combining them into a summary. This extractive approach relied heavily on features like word and phrase frequencies, title words, and location within the text. As computational power increased, more sophisticated linguistic techniques were applied to summarization in the 1990s and 2000s. These abstrative methods attempted to produce novel sentences that rephrased and condensed the original text, rather than simply extracting verbatim sentences. Machine learning algorithms were also introduced, using techniques like hidden Markov models and log-linear models to automatically learn summarization strategies from training data. More recently, neural network models like sequence-to-sequence models and transformer architectures have achieved state-of-the-art performance on abstractive summarization tasks.
Summarize the following passage on the history of summarization techniques:

The origins of summarization can be traced back to the advent of automated abstracting in the late 1950s and 1960s. Early work focused on extracting key sentences from text and combining them into a summary. This extractive approach relied heavily on features like word and phrase frequencies, title words, and location within the text. As computational power increased, more sophisticated linguistic techniques were applied to summarization in the 1990s and 2000s. These abstrative methods attempted to produce novel sentences that rephrased and condensed the original text, rather than simply extracting verbatim sentences. Machine learning algorithms were also introduced, using techniques like hidden Markov models and log-linear models to automatically learn summarization strategies from training data. More recently, neural network models like sequence-to-sequence models and transformer architectures have achieved state-of-the-art performance on abstractive summarization tasks.
user
The history of summarization techniques began with extractive methods in the 1950s-1960s, progressed to abstrative linguistic techniques in the 1990s-2000s using machine learning models, and has most recently seen success with neural network models for abstrative summarization.
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.
