Summarize the following text, which discusses the challenges of abstractive text summarization:

Abstractive text summarization is widely regarded as a more challenging task than extractive summarization, which selects and concatenates salient text segments from the input. Abstractive summarization involves generating new sentences that capture the essence of the source material, but do not necessarily reuse full phrases or clauses. This requires a deeper level of language understanding and generation capabilities.

One of the main challenges in abstractive summarization is preserving factual accuracy and conveying the key information from the source text in a concise and coherent manner. Generating grammatically correct and semantically meaningful new sentences is a complex natural language generation task, as the summarization model must learn to paraphrase the content, perform inference, and produce fluent and informative output.

Another challenge is maintaining consistency and avoiding contradictions or factual errors when generating new text that does not exist verbatim in the input. Abstractive models must be able to capture and represent the semantic relationships and implications within the source material, which requires advanced reasoning capabilities.

Additionally, abstractive summarization models often struggle with handling low-frequency or out-of-domain content, as they may lack sufficient training data or linguistic knowledge to accurately represent such information in a condensed form. This can limit the applicability of these models in real-world scenarios where diverse and dynamic content is encountered.
Summarize the following text, which discusses the challenges of abstractive text summarization:

Abstractive text summarization is widely regarded as a more challenging task than extractive summarization, which selects and concatenates salient text segments from the input. Abstractive summarization involves generating new sentences that capture the essence of the source material, but do not necessarily reuse full phrases or clauses. This requires a deeper level of language understanding and generation capabilities.

One of the main challenges in abstractive summarization is preserving factual accuracy and conveying the key information from the source text in a concise and coherent manner. Generating grammatically correct and semantically meaningful new sentences is a complex natural language generation task, as the summarization model must learn to paraphrase the content, perform inference, and produce fluent and informative output.

Another challenge is maintaining consistency and avoiding contradictions or factual errors when generating new text that does not exist verbatim in the input. Abstractive models must be able to capture and represent the semantic relationships and implications within the source material, which requires advanced reasoning capabilities.

Additionally, abstractive summarization models often struggle with handling low-frequency or out-of-domain content, as they may lack sufficient training data or linguistic knowledge to accurately represent such information in a condensed form. This can limit the applicability of these models in real-world scenarios where diverse and dynamic content is encountered.
user
Abstractive text summarization involves generating new sentences that capture the essence of the source material, rather than simply selecting and concatenating existing text segments. This task presents several challenges: 1) Preserving factual accuracy and conveying key information concisely and coherently while generating new text. 2) Maintaining consistency and avoiding contradictions or factual errors in the generated text. 3) Handling low-frequency or out-of-domain content effectively, as models may lack sufficient training data or linguistic knowledge for accurate representation. Overcoming these challenges requires advanced language understanding, reasoning capabilities, and natural language generation skills.
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: Please summarize how Asian American and Pacific Islander Heritage Month was started based on the text below: 

History
A former congressional staffer in the 1970s, Jeanie Jew, first approached Representative Frank Horton with the idea of designating a month to recognize Asian Pacific Americans, following the bicentennial celebrations.[19] In June 1977, Representatives Horton, and Norman Y. Mineta, introduced a United States House of Representatives resolution to proclaim the first ten days of May as Asian-Pacific Heritage Week.[20][21] A similar bill was introduced in the Senate a month later by Daniel Inouye and Spark Matsunaga.[20][22]

The proposed resolutions sought that May be designated for two reasons. For on May 7, 1843, the first Japanese immigrant arrived in the United States.[23][24][25] More than two decades later, on May 10, 1869, the golden spike was driven into the first transcontinental railroad, which was completed using Chinese labor.[23][24][26]

President Jimmy Carter signed a joint resolution for the celebration on October 5, 1978.[20]

On May 1, 2009, President Barack Obama signed Proclamation 8369, recognizing the month of May as Asian American and Pacific Islander Heritage Month.[1][27]
Response: A former congressional staffer with the idea approached a representative about it in the 1970s. In 1977, a resolution was introduced in the House and the Senate, and the President signed it in 1978. However, that resolution was only for the first ten days of May. It wasn't until 2009 that the whole month of May was named Asian American and Pacific Islander Heritage Month.

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.
