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.

Provide a concise summary for the following passage about the evaluation of automatic text summarization systems:

The evaluation of automatic summarization systems is a crucial but complex challenge given the diverse requirements of real-world applications. Traditional metrics like ROUGE assess summary content by measuring n-gram overlap with reference summaries, but do not reflect linguistic quality, coherence, or task specificity.

Recently, more comprehensive evaluation techniques taking into account multiple dimensionalities have emerged. Beyond just content selection and fidelity, modern frameworks consider overall responsiveness to the summarization intent, factual consistency, coherence and readability, ethical and social impacts, bias and representational harms, and efficiency/scalability.

Cloud-based human evaluation services provide ground truth ratings on various criteria by aggregating annotations from multiple judges. Complementary, computational models like BERT or GPT may be leveraged to automate quality assessments, filter out low-quality outputs before human review, and provide feedback for improving summarization models through techniques like reinforcement learning or adversarial training.

Overall, a well-rounded summarization system evaluation should combine traditional content metrics along with rigorous qualitative assessment by subject matter experts, responsible AI checks, end user feedback, and scalability/efficiency measures aligned to the target application. Summarization is inherently an assistive AI technology, so rigorously validating usability, safety and robustness is paramount.
user
The passage discusses the importance and complexity of evaluating automatic text summarization systems. Traditional metrics like ROUGE assess content overlap with reference summaries but do not reflect other key dimensions. Comprehensive modern evaluation frameworks consider responsiveness to intent, factual consistency, coherence, readability, social impacts, bias, and efficiency/scalability. Approaches involve human evaluation services, computational models for quality assessment and filtering, and end user feedback. A well-rounded evaluation combines content metrics with qualitative expert reviews, responsible AI checks, and validation of real-world usability, safety, and robustness, as summarization is an assistive AI technology.
assistant