Summarize the following details about the challenges and research directions in text summarization:

Despite significant progress, automatic text summarization remains a challenging task due to the complexity of natural language and the need for systems to understand and reason about the content at a deep level. One major challenge is handling context and background knowledge, as summarization often requires inferring implicit information and resolving ambiguities. Another challenge is generating coherent and fluent summaries, especially in abstractive approaches, where the system must learn to compose grammatically correct and logically structured text. Additionally, summarization systems must be able to handle diverse domains and genres of text, as well as cater to different summary lengths and styles based on user preferences and requirements. Current research focuses on developing more robust and generalizable models that can handle these challenges, as well as exploring techniques for controllable and customizable summarization, where users can specify desired attributes or constraints for the generated summaries. Other areas of interest include multi-document and multi-modal summarization, where information from multiple sources or modalities (text, images, videos) must be synthesized, and summarization for specific applications like question answering, dialogue systems, or content recommendation.
Summarize the following details about the challenges and research directions in text summarization:

Despite significant progress, automatic text summarization remains a challenging task due to the complexity of natural language and the need for systems to understand and reason about the content at a deep level. One major challenge is handling context and background knowledge, as summarization often requires inferring implicit information and resolving ambiguities. Another challenge is generating coherent and fluent summaries, especially in abstractive approaches, where the system must learn to compose grammatically correct and logically structured text. Additionally, summarization systems must be able to handle diverse domains and genres of text, as well as cater to different summary lengths and styles based on user preferences and requirements. Current research focuses on developing more robust and generalizable models that can handle these challenges, as well as exploring techniques for controllable and customizable summarization, where users can specify desired attributes or constraints for the generated summaries. Other areas of interest include multi-document and multi-modal summarization, where information from multiple sources or modalities (text, images, videos) must be synthesized, and summarization for specific applications like question answering, dialogue systems, or content recommendation.
user
Text summarization remains challenging due to the complexity of understanding natural language, handling context and background knowledge, generating coherent summaries, and handling diverse domains and genres. Current research focuses on developing more robust and generalizable models, controllable summarization, multi-document and multi-modal summarization, and summarization for specific applications like question answering and dialogue systems. Key challenges include inferring implicit information, resolving ambiguities, and composing grammatically correct and logically structured summaries.
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.
