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 2-3 sentences, summarize these paragraphs about the Shanghai Dragons.

Here are the paragraphs I'm referring to.

Around this time last year, the professional Overwatch team Shanghai Dragons had just finished up a history-making 0-40 season. At the time, nobody would have expected that just one year later they’d be leaving the league’s top three teams in a heap of black-and-blue bodies on the ground. Maybe they should change their name to the Shanghai Phoenixes.

Overwatch League season two’s third stage culminated in a mini-tournament over the weekend with hundreds of thousands of dollars on the line. Shanghai Dragons came out on top. They had the hardest possible path to this championship: They barely made it into the stage finals as the eighth seed, and then they took out the league’s three best teams—New York Excelsior, Vancouver Titans, and San Francisco Shock—back to back to back.
Response: Last season, the Shanghai Dragons finished a shockingly awful 0-40 season. This year, the Dragons just won a mini-tournament against some of the league's top teams. The Dragons entered the stage finals as the eighth seed before knocking out the Excelsior, Titans, and Shock in three straight games.

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 following text about the challenges of automatic text summarization:

Automatic text summarization, the process of using algorithms and computational techniques to generate concise summaries from larger texts, presents several significant challenges. Some of the key challenges in automatic summarization include:

1. Semantic understanding: Machines must be able to comprehend the meaning and context of the text, which is a complex task due to the nuances and ambiguities of natural language.

2. Identifying relevance: Algorithms must accurately identify the most important and relevant information within a text, often requiring advanced natural language processing techniques.

3. Generating coherent summaries: Automatic summaries should be logically structured, coherent, and flow naturally, which is difficult for machines to achieve without a deep understanding of language and discourse.

4. Handling redundancy: Effective summaries should avoid repeating the same information, but identifying and removing redundancies can be challenging for algorithms.

5. Preserving key details: While summarization requires condensing information, it is crucial to retain important details, facts, and context, which can be lost if algorithms are not carefully designed.

6. Domain-specific knowledge: Summarization algorithms may need to incorporate domain-specific knowledge and language models to produce accurate summaries in specialized fields like medicine or law.

7. Evaluation challenges: Assessing the quality and effectiveness of automatic summaries is a complex task, as human evaluation is subjective and automated evaluation metrics may not fully capture the nuances of language.

Overcoming these challenges is an active area of research, as efficient and accurate automatic summarization could have widespread applications in industries and fields where large amounts of text need to be processed and summarized.
user
The text discusses the significant challenges faced in automatic text summarization, including semantic understanding of text, identifying relevant information, generating coherent and natural-sounding summaries, handling redundancy, preserving key details, incorporating domain-specific knowledge, and evaluating the quality of automatic summaries. Overcoming these challenges is crucial for developing effective automatic summarization systems with widespread applications.
assistant