Automatic text summarization is a challenging task in natural language processing that involves creating concise and coherent summaries from lengthy input texts. There are two main approaches to automatic summarization: extractive and abstractive. Extractive summarization selects and concatenates the most informative sentences from the original text, while abstractive summarization generates entirely new sentences that capture the key information from the input. Explain the differences between extractive and abstractive summarization methods, their advantages and limitations, and the current state-of-the-art techniques used in each approach.
Automatic text summarization is a challenging task in natural language processing that involves creating concise and coherent summaries from lengthy input texts. There are two main approaches to automatic summarization: extractive and abstractive. Extractive summarization selects and concatenates the most informative sentences from the original text, while abstractive summarization generates entirely new sentences that capture the key information from the input. Explain the differences between extractive and abstractive summarization methods, their advantages and limitations, and the current state-of-the-art techniques used in each approach.
user
Extractive and abstractive summarization represent two distinct approaches to automatic text summarization, each with its own strengths and limitations.

Extractive summarization involves selecting the most salient sentences from the original text and combining them to form a summary. This method has the advantage of preserving the original wording and ensuring the factual accuracy of the summary. It is generally faster and simpler to implement compared to abstractive methods. However, extractive summaries can sometimes lack coherence or fail to capture the overall meaning of the text, as they are restricted to using existing sentences without rephrasing or restructuring.

On the other hand, abstractive summarization generates entirely new sentences that capture the key information from the input text. This approach allows for more coherent and concise summaries that better convey the overall meaning and can potentially handle paraphrasing and generalization. However, abstractive summarization is a more complex task that requires a deeper understanding of natural language and the ability to generate grammatically correct and semantically meaningful sentences. It also carries the risk of introducing factual errors or omitting important details present in the original text.

Current state-of-the-art techniques for extractive summarization often involve ranking and selecting sentences based on various features, such as word frequency, position in the text, and semantic similarity to the overall document. Machine learning models, particularly neural networks, are commonly used for this task. Abstractive summarization, on the other hand, often utilizes sequence-to-sequence models, which generate summaries token by token based on the input text and learned patterns from large datasets.

While both approaches have their merits, the choice between extractive and abstractive summarization depends on the specific requirements and constraints of the task, such as the desired level of compression, the importance of preserving factual accuracy, and the availability of computational resources and training data.
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: Few people would have predicted going into 2022 that it would finally be the year Netflix’s gangbusters growth stalled out. During consecutive quarters this year, the longtime leading streaming site saw year-to-year declines in subscribers, marking the first time that had happened in 10 years.

By the time the streamer reported third-quarter results, its growth was back on track, but the damage was clearly done. NetflixNFLX +1% fast-tracked its new ad-supported tier and began testing a system for banning password sharing. Those moves will be closely scrutinized in 2023, with observers keen to see whether consumers will accept major changes to the very DNA of the popular streamer.

Netflix’s subscriber loss was certainly one of the biggest surprise headlines of 2022. What will be this year’s? Here are five streaming trends to watch for in 2023.


Both Disney+ and Netflix recently introduced ad-supported platforms, and AmazonAMZN -3.7% is accepting ads for Thursday Night Football. How consumers receive the lower-priced, ad-supported tiers may determine whether other platforms adopt similar approaches.

While some initial reports suggested Netflix didn’t meet early advertising promises, this week, CFRA raised its stock price target for the streamer based on a strong outlook for the ad-supported platform. “We think it will be difficult for competitors to catch NFLX, one of the few profitable streaming providers with global scale,” wrote CFRA analyst Kenneth Leon.


The specter of the disastrous CNN+ launch will loom large in 2023. Where it once seemed the market was saturated with 800-pound gorillas like Disney+ and HBO Max, the fast folding of CNN+ and the takeaways from that will serve to keep future niche launches more limited. With people reaching a saturation point with their new subscriptions, it’s a big risk to launch something with limited appeal.

Disney+ has crowed about moving ahead of Netflix in subscribers—which, granted, includes Hulu and ESPN bundles as well. But Disney+ has seen outstanding growth, including adding 12.1 million subscribers in third quarter to boost the three-way total to more than 235 million, or 12 million more than Netflix. Analysts have said they anticipate the recent launch of Disney+’s ad-supported service will push Disney+ even farther ahead in the streaming wars.
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Based on the above text, what are some key trends in the streaming industry?
Response: Ad-supported tiers are growing in popularity among streaming providers as they leverage their global audience and scale. Furthermore, launches of niche products will slow down given the poor reception of CNN+ and as people reach a personal limit of streaming subscriptions. On the flip side, Disney+ is experiencing rampant growth and is expected to pull ahead of the competition in terms of subscriber numbers with the launch of its own ad-supported tier. 

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.
