Summarize the following passage about the history of summarization techniques:

Ancient thinkers like Aristotle and Pliny the Elder recognized the value of summarization as a technique for condensing lengthy works into more concise formats. However, automated summarization methods did not emerge until the 20th century. In 1958, researchers at IBM demonstrated one of the earliest examples of an automatic summarization system that could generate abstracts for research papers. Over subsequent decades, summarization systems steadily evolved with advances in natural language processing and machine learning algorithms.

In the early days, most summarization systems relied on heuristic methods and statistical techniques to identify and extract the most salient sentences from a document. These extractive summarization approaches aimed to cover the key points while minimizing redundancy. As computing power and data availability increased, researchers explored more sophisticated data-driven techniques powered by deep learning models. Modern neural network architectures enable abstractive summarization, generating new sentences that capture the essence of the source material in a concise, coherent way.

Summarization research has expanded rapidly in recent years, fueled by the growing demand for tools to efficiently distill information from the ever-increasing volume of digital content. While early efforts focused mainly on text summarization, current research also targets other modalities like images, videos, and audio. Summarization systems now assist diverse applications, from automated news aggregation and report generation to conversational AI assistants and educational tools.
Summarize the following passage about the history of summarization techniques:

Ancient thinkers like Aristotle and Pliny the Elder recognized the value of summarization as a technique for condensing lengthy works into more concise formats. However, automated summarization methods did not emerge until the 20th century. In 1958, researchers at IBM demonstrated one of the earliest examples of an automatic summarization system that could generate abstracts for research papers. Over subsequent decades, summarization systems steadily evolved with advances in natural language processing and machine learning algorithms.

In the early days, most summarization systems relied on heuristic methods and statistical techniques to identify and extract the most salient sentences from a document. These extractive summarization approaches aimed to cover the key points while minimizing redundancy. As computing power and data availability increased, researchers explored more sophisticated data-driven techniques powered by deep learning models. Modern neural network architectures enable abstractive summarization, generating new sentences that capture the essence of the source material in a concise, coherent way.

Summarization research has expanded rapidly in recent years, fueled by the growing demand for tools to efficiently distill information from the ever-increasing volume of digital content. While early efforts focused mainly on text summarization, current research also targets other modalities like images, videos, and audio. Summarization systems now assist diverse applications, from automated news aggregation and report generation to conversational AI assistants and educational tools.
user
Summarization has ancient roots, but automated methods emerged in the 20th century with systems that used heuristics and statistical techniques for extractive summarization. Recent advances in deep learning enabled more sophisticated abstractive summarization models that generate coherent summaries. Current research explores summarization across various media types to meet growing demands for distilling information efficiently.
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: In under five sentences, summarize this information about Dwayne Johnson -- 

American actor and former professional wrestler Dwayne Johnson boasts a mixed heritage. His mother is Samoan, while his father is African Canadian.

As a teen, Johnson had his mind set on playing professional football. The talented athlete even received a full scholarship to the University of Miami. Unfortunately, he was unable to play much due to a series of injuries. After graduating, Johnson signed up with a team in the Canadian Football League. But he was cut two months into the season.

Johnson returned to the US to try his hand at wrestling. His father and maternal grandfather had both been professional wrestlers. He joined the World Wrestling Federation (WWF) in 1996 and won his first title within a few months. The charismatic athlete quickly became one of the most successful and popular wrestlers of his era.

In 2001, Johnson, now nicknamed "The Rock," decided to try acting. He landed his first lead role in The Scorpion King a year later. Johnson's $5.5 million pay set a new record for first-time actors. He has since starred in several action films and franchises like Fast and Furious and Jumanji. Johnson is now one of the highest-paid stars in Hollywood.

Johnson frequently pays homage to his Samoan heritage in his social media posts. In 2019, he brought the Samoan culture to the big screen in Fast & Furious Presents: Hobbs & Shaw.
Response: Dwayne Johnson (The Rock) is Samoan and African Canadian. He dreamt of playing football professionally, but a series of injuries meant he was cut two months into playing in the Canadian Football League. He joined the World Wrestling Federation (WWF) in 1996, quickly becoming popular and winning his first title within a few months. He began acting in 2001, starring in movies such as The Scorpion Year, Fast and Furious, and Jumanji.  

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.
