Summarize the following text on the key principles and challenges of multi-document summarization:

Multi-document summarization is the task of automatically generating a concise summary by combining and synthesizing information from multiple source documents on the same topic. This process is more challenging than single-document summarization because it requires understanding and integrating information from multiple perspectives and sources.

One key principle of multi-document summarization is content selection, which involves identifying and extracting the most relevant and salient information from the source documents. This process often involves techniques such as topic modeling, clustering, and graph-based ranking algorithms to determine the importance and centrality of different concepts and information units.

Another important principle is information fusion, which involves combining and synthesizing information from different sources to create a coherent and non-redundant summary. This may involve resolving contradictions, identifying overlapping or complementary information, and generating new sentences or phrases to convey the integrated information.

Multi-document summarization systems also need to address issues such as redundancy elimination, temporal ordering of events, and maintaining the readability and coherence of the generated summary. Additionally, handling noisy or unreliable sources and dealing with domain-specific terminology and jargon can be challenging.

Overall, multi-document summarization is a complex task that requires sophisticated natural language processing techniques and the ability to reason over multiple sources of information. It has applications in areas such as news aggregation, scientific literature review, and intelligence analysis, where synthesizing information from multiple sources is crucial.
Summarize the following text on the key principles and challenges of multi-document summarization:

Multi-document summarization is the task of automatically generating a concise summary by combining and synthesizing information from multiple source documents on the same topic. This process is more challenging than single-document summarization because it requires understanding and integrating information from multiple perspectives and sources.

One key principle of multi-document summarization is content selection, which involves identifying and extracting the most relevant and salient information from the source documents. This process often involves techniques such as topic modeling, clustering, and graph-based ranking algorithms to determine the importance and centrality of different concepts and information units.

Another important principle is information fusion, which involves combining and synthesizing information from different sources to create a coherent and non-redundant summary. This may involve resolving contradictions, identifying overlapping or complementary information, and generating new sentences or phrases to convey the integrated information.

Multi-document summarization systems also need to address issues such as redundancy elimination, temporal ordering of events, and maintaining the readability and coherence of the generated summary. Additionally, handling noisy or unreliable sources and dealing with domain-specific terminology and jargon can be challenging.

Overall, multi-document summarization is a complex task that requires sophisticated natural language processing techniques and the ability to reason over multiple sources of information. It has applications in areas such as news aggregation, scientific literature review, and intelligence analysis, where synthesizing information from multiple sources is crucial.
user
The given text discusses multi-document summarization, which is the task of generating a concise summary by combining and synthesizing information from multiple source documents on the same topic. It outlines two key principles: content selection, which involves identifying and extracting the most relevant information from the sources, and information fusion, which involves combining and synthesizing information from different sources to create a coherent and non-redundant summary. The text also highlights challenges such as redundancy elimination, maintaining temporal ordering and readability, handling noisy sources, and dealing with domain-specific terminology. Multi-document summarization is a complex task that requires sophisticated natural language processing techniques and the ability to reason over multiple sources of information. It has applications in areas where synthesizing information from multiple sources is crucial, such as news aggregation, scientific literature review, and intelligence analysis.
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: Given the following article, write a summary of the key ideas.

This is the article:
Despite the city’s support, the number of farms in Tokyo has been slowly declining for years, by nearly 14.5 percent between 2005 and 2015. Over the same period, the average age of Tokyo’s farmers increased by 3.3 years, to nearly 64. Today, Ryoto Matsuzawa says there are 1,250 farms left, most of which are under one hectare in size. “We want to preserve those at all costs.”

The city recently made a crucial decision to do so. Eighty percent of the farms protected under the Law on Productive Green Areas were registered the year that law was enacted, in 1992. The law stipulates that after 30 years, the landowners can ask the city to buy their land at market price. But most cities, strapped for cash, can’t afford to. If they don’t, the land loses its tax subsidy and can be sold for commercial development.

In other words, about 80 percent of the farms protected under the law were eligible to come up for sale in 2022. “These areas are nearly 45 percent of the agricultural land in Tokyo,” says Ryoto Matsuzawa.
Response: Between 2005 and 2015, the number of farms in Tokyo decreased by 14.5 percent and the average age of farmers increased by 3.3 years. Preserving Tokyo's remaining 1,250 small farms feels priceless to many, but there is an ongoing issue where many cities cannot afford to buy the land protected under the Productive Green Areas Law at market value, as legally required. This leads to the land being lost for agricultural use, and usually being sold for commercial development.

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.
