Summarize the following text about the history and evolution of summarization techniques: Summarization, the process of condensing text into a concise version, has been studied extensively in computer science, linguistics, and cognitive science. Early approaches focused on extractive summarization, which involves identifying and extracting the most salient sentences from a document to form a summary. Techniques included scoring sentences based on statistical and linguistic features like word frequency, cue phrases, and position in the text. In the late 1990s, researchers began exploring abstractive summarization methods that generate novel sentences by understanding the semantic content and generating new formulations, rather than simply extracting sentences verbatim. This required advances in natural language processing and generation. Recent neural network approaches using encoder-decoder architectures and attention mechanisms have achieved state-of-the-art performance on many benchmark datasets. However, challenges remain in generating coherent, factual, and abstractive summaries for long documents and specialised domains.
Summarize the following text about the history and evolution of summarization techniques: Summarization, the process of condensing text into a concise version, has been studied extensively in computer science, linguistics, and cognitive science. Early approaches focused on extractive summarization, which involves identifying and extracting the most salient sentences from a document to form a summary. Techniques included scoring sentences based on statistical and linguistic features like word frequency, cue phrases, and position in the text. In the late 1990s, researchers began exploring abstractive summarization methods that generate novel sentences by understanding the semantic content and generating new formulations, rather than simply extracting sentences verbatim. This required advances in natural language processing and generation. Recent neural network approaches using encoder-decoder architectures and attention mechanisms have achieved state-of-the-art performance on many benchmark datasets. However, challenges remain in generating coherent, factual, and abstractive summaries for long documents and specialised domains.
user
Summarization techniques have evolved from early extractive methods that identified and extracted key sentences, to recent neural approaches that can generate novel abstractive summaries by understanding semantic content. However, generating coherent and factual summaries for long and specialised texts remains challenging.
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.
