Summarize the salient points from this text on abstractive summarization approaches:

Abstractive summarization aims to generate concise and fluent summaries that capture the key information from the input text while rephrasing the content using natural language generation techniques. Sequence-to-sequence (seq2seq) models, which encode the input text into a compact representation and decode it into a summary, have been widely used for this task.

Several extensions to the basic seq2seq architecture have been proposed to improve the performance of abstractive summarizers. The copy mechanism allows the model to selectively copy words and phrases from the input text, alleviating the out-of-vocabulary issue. The coverage model keeps track of the input text coverage during decoding, promoting focus on under-summarized content.

Reinforcement learning techniques have also been explored, where the summarization model is trained to optimize metrics like ROUGE score or summary quality. This helps alleviate the exposure bias issue faced by traditional maximum likelihood training. More recently, pre-training the summarization model on large corpora and fine-tuning it for specific domains has shown promising results.

Abstractive summarizers, however, often face challenges like factual inconsistencies, where the generated summary deviates from the facts present in the input text. Incorporating fact checking modules or grounding the summary generation process with the input text has been proposed to mitigate this issue.
Summarize the salient points from this text on abstractive summarization approaches:

Abstractive summarization aims to generate concise and fluent summaries that capture the key information from the input text while rephrasing the content using natural language generation techniques. Sequence-to-sequence (seq2seq) models, which encode the input text into a compact representation and decode it into a summary, have been widely used for this task.

Several extensions to the basic seq2seq architecture have been proposed to improve the performance of abstractive summarizers. The copy mechanism allows the model to selectively copy words and phrases from the input text, alleviating the out-of-vocabulary issue. The coverage model keeps track of the input text coverage during decoding, promoting focus on under-summarized content.

Reinforcement learning techniques have also been explored, where the summarization model is trained to optimize metrics like ROUGE score or summary quality. This helps alleviate the exposure bias issue faced by traditional maximum likelihood training. More recently, pre-training the summarization model on large corpora and fine-tuning it for specific domains has shown promising results.

Abstractive summarizers, however, often face challenges like factual inconsistencies, where the generated summary deviates from the facts present in the input text. Incorporating fact checking modules or grounding the summary generation process with the input text has been proposed to mitigate this issue.
user
The text discusses key approaches for abstractive summarization, which involves generating concise summaries by rephrasing the content. It covers sequence-to-sequence models as the primary architecture, along with extensions like copy mechanism to handle out-of-vocabulary words, coverage models to focus on under-summarized content, and reinforcement learning to optimize for summary quality metrics. Pre-training on large corpora and fine-tuning is also mentioned as a recent trend. The text also highlights the issue of factual inconsistencies in abstractive summaries and proposes potential solutions like fact checking modules.
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: Condense this film info for me into only two sentences: 

What is this film about?
The official synopsis for "Guardians of the Galaxy Vol. 3" from Marvel Studios reads:


"Peter Quill, still reeling from the loss of Gamora, must rally his team around him to defend the universe along with protecting one of their own. A mission that, if not completed successfully, could quite possibly lead to the end of the Guardians as we know them."

As evidenced from the film's trailers, it seems as though the Guardians will be protecting Rocket from the High Evolutionary, as the two of them apparently have an intense backstory.

PHOTO: Rocket (voiced by Bradley Cooper) is shown in Marvel Studios' Guardians of the Galaxy Vol. 3.
Rocket (voiced by Bradley Cooper) is shown in Marvel Studios' Guardians of the Galaxy Vol. 3.
Courtesy of Marvel Studios
Cooper confirmed as much when speaking to "GMA" at the film's premiere, saying, "Honestly, it was very emotional to play [Rocket]."

MORE: Bradley Cooper reveals what playing Rocket meant to him in 'Guardians of the Galaxy'
"You really get a chance to understand how he came to be," Cooper continued. "Rocket went through a lot."

PHOTO: Chukwudi Iwuji appears as The High Evolutionary in Marvel Studios' Guardians of the Galaxy Vol. 3.
Chukwudi Iwuji appears as The High Evolutionary in Marvel Studios' Guardians of the Galaxy Vol. 3.
Courtesy of Marvel Studios
Iwuji teased the High Evolutionary's villainy, saying, "He is such a great villain to play. I had so much fun playing him."

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"He's bad ... he's really bad," he added. "He really does do some horrible things, but it's a joy to play those [roles]."
Response: Guardians of the Galaxy Vol. 3 focuses on Peter Quill, who is still getting over Gamora's death, as he and the others protect Rocket from the High Evolutionary. You get to see how Rocket came to be and a great villain who does horrible things. 

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.
