Summarize the following text about the history of summarization techniques:

Automatic text summarization has a long history, dating back to the 1950s and 1960s when researchers began exploring ways to automatically condense and extract key information from texts. Early work focused on statistical techniques, such as word and phrase frequency analysis, to identify important content. In the 1990s, with the rise of the Internet and digital information, summarization gained renewed interest as a means to manage the growing volume of online content. Researchers began developing more advanced algorithms that leveraged techniques from natural language processing, machine learning, and information retrieval. Significant progress was made in extractive summarization, which selects and concatenates the most salient sentences from a text to form a summary. More recently, advancements in deep learning and large language models have enabled the development of abstractive summarization methods that can generate human-like summaries by understanding the text and rephrasing key information in a concise and coherent manner. Today, automatic text summarization is widely used in various applications, from news aggregation and academic research to customer service and business intelligence, helping users quickly grasp the essence of lengthy documents and information sources.
Summarize the following text about the history of summarization techniques:

Automatic text summarization has a long history, dating back to the 1950s and 1960s when researchers began exploring ways to automatically condense and extract key information from texts. Early work focused on statistical techniques, such as word and phrase frequency analysis, to identify important content. In the 1990s, with the rise of the Internet and digital information, summarization gained renewed interest as a means to manage the growing volume of online content. Researchers began developing more advanced algorithms that leveraged techniques from natural language processing, machine learning, and information retrieval. Significant progress was made in extractive summarization, which selects and concatenates the most salient sentences from a text to form a summary. More recently, advancements in deep learning and large language models have enabled the development of abstractive summarization methods that can generate human-like summaries by understanding the text and rephrasing key information in a concise and coherent manner. Today, automatic text summarization is widely used in various applications, from news aggregation and academic research to customer service and business intelligence, helping users quickly grasp the essence of lengthy documents and information sources.
user
Automatic text summarization has evolved significantly over decades. Early techniques relied on statistical methods like word frequency analysis. With the rise of digital information, more advanced algorithms using natural language processing and machine learning were developed for extractive summarization. Recent advancements in deep learning and large language models have enabled abstractive summarization, generating human-like summaries by rephrasing key information. Summarization is now widely used in various applications to quickly convey the essence of lengthy texts.
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: What does this article say about Uber? Text below:

Two brothers who drive for Uber recently conducted an experiment. They opened their Uber apps while sitting in the same room, and tested which brother could earn more money to do the same work.

In a video published on The Rideshare Guy YouTube channel, the brothers recorded themselves looking for rides on the app. They found that Uber showed them nearly identical jobs, but offered to pay one of them a little better. The siblings could only guess why. Had Uber's algorithm somehow calculated their worth differently?

University of California College of the Law professor Veena Dubal says that's exactly what's going on. In a recent paper, she says rideshare apps promote "algorithmic wage discrimination" by personalizing wages for each driver based on data they gather from them. The algorithms are proprietary, so workers have no way of knowing how their data is being used, Dubal says.

"The app is their boss," Dubal told Morning Edition's A Martinez. "But unlike a human boss who you can negotiate with or withhold information from, the algorithms know so much about these workers."

Uber says workers who drive electric vehicles get a $1 bonus per ride, but the company does not use drivers' personal data to set their pay rates. "Uber does not personalize fares to individual drivers, and a driver's race, ethnicity, acceptance rate, total earnings, or prior trip history are not considered when calculating fares," a spokesperson writes in a statement. A representative for Lyft calls Dubal's paper "biased", saying it relies on cherry-picked data and debunked anecdotal information.

Personalized digitized pay is already the new normal in some workplaces, according to Dubal, and it's begun to attract attention from regulators. 
Response: This article states that Uber was paying one brother more to do the same job. It also says that a spokesperson for Uber doesn't personalize fares according to personal data, but that workers who drive electric vehicles get a bonus every ride.

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.
