Summarize the key ideas and future research directions outlined in this text on multi-document and cross-lingual summarization: Multi-document summarization involves generating a coherent summary from a cluster of related documents, while cross-lingual summarization deals with summarizing text in one language based on sources in other languages. These tasks introduce unique challenges, such as handling redundancy, resolving contradictions, and preserving important context and relationships across multiple documents or languages. Recent research has explored techniques like graph-based representations, discourse modeling, and cross-lingual transfer learning to address these challenges. However, generating coherent and informative summaries that capture the most salient information across multiple sources and languages remains an active area of research. Future work aims to improve context modeling, develop more robust cross-lingual representations, and leverage large multilingual language models for better handling of linguistic and cultural nuances.
Summarize the key ideas and future research directions outlined in this text on multi-document and cross-lingual summarization: Multi-document summarization involves generating a coherent summary from a cluster of related documents, while cross-lingual summarization deals with summarizing text in one language based on sources in other languages. These tasks introduce unique challenges, such as handling redundancy, resolving contradictions, and preserving important context and relationships across multiple documents or languages. Recent research has explored techniques like graph-based representations, discourse modeling, and cross-lingual transfer learning to address these challenges. However, generating coherent and informative summaries that capture the most salient information across multiple sources and languages remains an active area of research. Future work aims to improve context modeling, develop more robust cross-lingual representations, and leverage large multilingual language models for better handling of linguistic and cultural nuances.
user
The given text discusses key ideas and future research directions in multi-document and cross-lingual text summarization. Multi-document summarization involves summarizing from multiple related documents, while cross-lingual summarization deals with summarizing across different languages. Key challenges include handling redundancy, resolving contradictions, and preserving context and relationships across documents/languages. Recent research explores techniques like graph representations, discourse modeling, and cross-lingual transfer learning. However, generating coherent and informative summaries across multiple sources and languages remains an active research area. Future work aims to improve context modeling, develop robust cross-lingual representations, and leverage large multilingual language models for better handling of linguistic and cultural nuances.
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 are the key points of this news article?


The former chief security officer for Uber was sentenced to probation Thursday for trying to cover up a 2016 data breach in which hackers accessed tens of millions of customer records from the ride-hailing service.

Joseph Sullivan was sentenced to a three-year term of probation and ordered to pay a fine of $50,000, the U.S. attorney’s office announced.

Sullivan, 54, of Palo Alto was convicted by a federal jury in San Francisco last October of obstructing justice and concealing knowledge that a federal felony had been committed.
Response: Joseph Sullivan was sentenced to a three-year term of probation for covering up a data breach at Uber. The data breach in which hackers accessed millions of customers’ records. Joseph also has to pay a fine of $50,000 he was convicted last October in San Francisco. 

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.
