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 short, what does this article say Abyssinian ground hornbills typically eat in their natural habitat?

Primarily carnivores, Abyssinian ground hornbills eat reptiles such as cobras, tortoises and lizards, as well as mammals like hares and mongooses, and insects and arachnids. Occasionally they will scavenge for carrion to supplement their diet, as well as consume seeds and fruit. Abyssinian ground hornbills will travel up to seven miles (11 kilometers) each day in search of food, overtaking and consuming prey they encounter. These animals have been observed following ungulate herds and forest fires to feed on prey fleeing disturbances.
At the Smithsonian's National Zoo, Abyssinian ground hornbills are given Nebraska Brand Feline Diet, mice, mealworms and crickets.
Response: Abyssinian ground hornbills typically eat reptiles, mammals, and insects as they are carnivores, but they occasionally eat carrion, seeds, and fruit.  

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.

Summarize the core points made in the following article about different approaches to multi-document summarization:

Multi-document summarization is the task of automatically generating a concise summary from a collection of topically-related documents. Traditional single-document summarization approaches are often inadequate when dealing with multi-document inputs containing complementary and sometimes contradictory information.

Centroid-based methods are among the earliest used for multi-document summarization. These involve clustering related sentences and selecting representatives closest to the centroid of each cluster. Graph-based approaches construct graphs representing relationships between textual units like sentences or passages, then score and extract relevant components.

Recently, neural network models have achieved state-of-the-art performance on multi-document summarization benchmarks. Sequence-to-sequence models with attention mechanisms learn to read and integrate information across documents, then generate abstractive summaries reframing the key content.

Advanced techniques have explored extracting and synthesizing key facts from multiple sources, tracking and resolving contradictions, separating factual content from opinions and claims, and tailoring salience modeling for the summarization task. However, generating coherent summaries remains challenging, hindered by issues like dangling anaphoric references without context.

Overall, the ability to produce informative multi-document summaries capturing key insights across a collection of related texts enables efficient knowledge acquisition in our information-saturated world.
user
The article discusses approaches to multi-document summarization, which involves generating concise summaries from collections of related documents. Traditional methods like centroid-based clustering and graph-based scoring were limited. Recently, neural sequence-to-sequence models with attention have achieved state-of-the-art performance by learning to integrate information across documents to produce abstractive summaries. Advanced techniques explore fact synthesis, contradiction resolution, separating facts from opinions, and task-specific salience modeling. However, generating coherent summaries across documents remains challenging. Multi-document summarization enables efficient knowledge acquisition by summarizing key insights from related texts.
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