Summarize the key points from the following text on the history and techniques of text summarization:

Automatic text summarization is the process of producing a concise and fluent summary of one or more text documents. It reduces the time needed for a human to grasp the most important information from the source material. Text summarization has roots dating back to the 1950s, but became more widely researched with the rise of machine learning in the 1990s.

Early approaches focused on statistical techniques to extract and combine the most salient sentences or keywords from a text. Modern neural summarization models can grasp the meaning of texts and generate abstractive summaries rewritten in natural language. Common architectures include sequence-to-sequence models, transformers, and reinforcement learning.

Key challenges include addressing the information loss inherent in summarization, handling coherence between extracted elements, and improving factual accuracy, consistency and human-likeness of outputs. Evaluation methods such as ROUGE analyze the overlap between human and machine-generated summaries, while manual evaluation assesses qualities like coherence and information coverage.
Summarize the key points from the following text on the history and techniques of text summarization:

Automatic text summarization is the process of producing a concise and fluent summary of one or more text documents. It reduces the time needed for a human to grasp the most important information from the source material. Text summarization has roots dating back to the 1950s, but became more widely researched with the rise of machine learning in the 1990s.

Early approaches focused on statistical techniques to extract and combine the most salient sentences or keywords from a text. Modern neural summarization models can grasp the meaning of texts and generate abstractive summaries rewritten in natural language. Common architectures include sequence-to-sequence models, transformers, and reinforcement learning.

Key challenges include addressing the information loss inherent in summarization, handling coherence between extracted elements, and improving factual accuracy, consistency and human-likeness of outputs. Evaluation methods such as ROUGE analyze the overlap between human and machine-generated summaries, while manual evaluation assesses qualities like coherence and information coverage.
user
Text summarization is the process of generating a concise summary from one or more source documents. Early techniques relied on statistical methods to extract key sentences or keywords, while modern neural models can generate abstractive summaries rewritten in natural language. Challenges include minimizing information loss, maintaining coherence, and improving factual accuracy and human-likeness of outputs. Evaluation combines automated metrics like ROUGE with manual assessment of qualities like coherence and coverage.
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 information found in the following article, can you tell me the logic behind allowing the children to assist in designing their school's architecture?:


Classrooms are arranged around an indoor rainforest. Porthole windows that look like googly eyes peering out from lumpy, butter-colored walls. Zigzagging roofs and soaring archways. 
If this mind-bending vision sounds like something that might have been dreamt up by school pupils, that’s because it was – and now those dreams have become reality in the eccentric, off-kilter shape of Madrid’s Reggio school (main picture).

Designed by Spanish architect Andrés Jaque, the Reggio’s radical construction not only brings nature indoors but also encourages plant and animal life to colonize its ‘living’ exterior walls.
Clad in an insulating mixture of mashed cork, it offers an ideal habitat, not unlike the surface of a tree, for fungi, insects and microbial life. 

Classrooms look out onto gardens – some inaccessible to humans – created by ecologists to attract birds, butterflies, bats and bees. Inside, a courtyard hosts the miniature temperate rainforest which rises two stories to the glass canopy above. Labs and workshops are arranged around its edges.

The school’s design is a living embodiment of the radical Reggio-Emilia method, an educational philosophy developed in postwar Italy. It sees children not as empty vessels to be filled with education, but as active participants in defining their curriculum.
Experience – touching, listening and first-hand discovery – lie at the heart of the learning process. Connection with the outdoors is key and the physical environment is imagined as the ‘third teacher’, with spaces configured to encourage open-ended exploration.


Response: Because they share the same educational values as the radical Italian education reformer Reggio Emilia, the administration and teachers of this school invited the students to participate in the design of the facility. According to this approach, experiential learning is the foundation of education, and the best way to learn is to incorporate experiences like touching, hearing, and hands-on learning into any effective curriculum.

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.
