Please summarize the main points of the following text about the history of summarization techniques:

Automatic summarization has been studied since the late 1950s, with some of the earliest work dating back to 1958 when Hans Peter Luhn wrote about the idea of creating automatic abstracts. In the 1960s, researchers at IBM developed some of the foundational methods for automatic summarization, including the use of frequency-based statistics and discourse-level analysis.

Early summarization systems relied heavily on techniques from information retrieval, such as term frequency and position, to identify the most salient sentences within a text. In the 1990s, researchers began exploring the use of deeper natural language processing techniques, such as rhetorical structure analysis and lexical chains, to improve the quality of summaries.

More recently, with the rise of machine learning and large language models, data-driven approaches to summarization have become increasingly popular. These techniques train models on large datasets of text and summaries, learning to identify and generate summaries in a more holistic and context-aware way. Some popular neural summarization models include transformer-based architectures like BERT and GPT.
Please summarize the main points of the following text about the history of summarization techniques:

Automatic summarization has been studied since the late 1950s, with some of the earliest work dating back to 1958 when Hans Peter Luhn wrote about the idea of creating automatic abstracts. In the 1960s, researchers at IBM developed some of the foundational methods for automatic summarization, including the use of frequency-based statistics and discourse-level analysis.

Early summarization systems relied heavily on techniques from information retrieval, such as term frequency and position, to identify the most salient sentences within a text. In the 1990s, researchers began exploring the use of deeper natural language processing techniques, such as rhetorical structure analysis and lexical chains, to improve the quality of summaries.

More recently, with the rise of machine learning and large language models, data-driven approaches to summarization have become increasingly popular. These techniques train models on large datasets of text and summaries, learning to identify and generate summaries in a more holistic and context-aware way. Some popular neural summarization models include transformer-based architectures like BERT and GPT.
user
The text discusses the history of automatic summarization techniques. Early work in the 1950s and 1960s laid the foundations using frequency-based statistics and discourse analysis. In the 1990s, natural language processing techniques were explored. More recently, with the rise of machine learning, data-driven approaches using large language models have become popular for generating summaries in a context-aware manner.
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: 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.
