Summarize the following text, which provides an overview of summarization techniques used in natural language processing:

Summarization is the process of condensing a large amount of text into a concise summary that captures the key points and overall meaning. There are two main types of summarization: extractive and abstractive. Extractive summarization involves selecting and combining the most important sentences or phrases from the original text to form the summary. This method is relatively straightforward and can preserve the original language and factual details. Abstractive summarization, on the other hand, generates an entirely new text that captures the essence of the original content but uses different words and phrasing. This method requires a deeper understanding of the text and often involves advanced natural language processing techniques like semantics and discourse analysis. Both extractive and abstractive summarization can be performed manually or automatically using machine learning algorithms. Automatic summarization systems typically rely on statistical models and heuristics to identify and rank the most salient information in a text. Some common approaches include sentence scoring based on features like word frequency and position, topic modeling, and sequence-to-sequence models that generate summaries token by token. Evaluation of summarization systems can be challenging due to the inherent subjectivity of what constitutes a good summary. Metrics like ROUGE compare system-generated summaries to human-written reference summaries based on factors like n-gram overlap and longest common subsequences.
Summarize the following text, which provides an overview of summarization techniques used in natural language processing:

Summarization is the process of condensing a large amount of text into a concise summary that captures the key points and overall meaning. There are two main types of summarization: extractive and abstractive. Extractive summarization involves selecting and combining the most important sentences or phrases from the original text to form the summary. This method is relatively straightforward and can preserve the original language and factual details. Abstractive summarization, on the other hand, generates an entirely new text that captures the essence of the original content but uses different words and phrasing. This method requires a deeper understanding of the text and often involves advanced natural language processing techniques like semantics and discourse analysis. Both extractive and abstractive summarization can be performed manually or automatically using machine learning algorithms. Automatic summarization systems typically rely on statistical models and heuristics to identify and rank the most salient information in a text. Some common approaches include sentence scoring based on features like word frequency and position, topic modeling, and sequence-to-sequence models that generate summaries token by token. Evaluation of summarization systems can be challenging due to the inherent subjectivity of what constitutes a good summary. Metrics like ROUGE compare system-generated summaries to human-written reference summaries based on factors like n-gram overlap and longest common subsequences.
user
Summarization condenses large text into concise summaries, either extractively by selecting important sentences/phrases or abstractively by generating new text. Automatic summarization uses statistical models and techniques like sentence scoring, topic modeling, and sequence-to-sequence models. Evaluation compares system summaries to human references using metrics like ROUGE.
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.
