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: Can you summarize this in one or two sentences:

The V. C. Morris Gift Shop is located at 140 Maiden Lane in downtown San Francisco, California, United States, and was designed by Frank Lloyd Wright in 1948. The store was used by Wright as a physical prototype or proof of concept for the circular ramp at the Solomon R. Guggenheim Museum.[1]

All of the built-in furniture is constructed out of black walnut, is original to the renovation, and was designed by Manuel Sandoval, who apprenticed and worked with Frank Lloyd Wright.[2][3]

Response: Frank Lloyd Wright's V.C. Morris Gift Shop in downtown San Francisco was designed in 1948 and was a prototype for the circular ramp at the Guggenheim Museum. The black walnut built-in furniture was designed by Manuel Sandoval.

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 key challenges and limitations faced in automated text summarization using current techniques:

While automated text summarization systems have advanced significantly, several key challenges remain. Systems can struggle to grasp complex context, nuance, and implications present in text. They may miss important subtleties or distort meaning through oversimplification. Abstractive approaches that generate novel sentences often produce unnatural, incoherent, or factually inconsistent output. Systems are heavily influenced by the data they are trained on and can exhibit biases and inconsistencies. Multi-document and multi-lingual summarization pose additional difficulties in integrating and generating coherent summaries from diverse inputs. Evaluation of summary quality also remains a challenge without robust automated metrics fully capturing all dimensions of what constitutes a good summary.
user
Current automated text summarization systems face challenges in grasping complex context and nuance, missing important subtleties or distorting meaning through oversimplification. Abstractive approaches can generate unnatural, incoherent, or factually inconsistent output. Systems may exhibit biases and inconsistencies from their training data. Multi-document and multi-lingual summarization pose additional difficulties. Robust automated evaluation metrics fully capturing summary quality are also lacking.
assistant