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: Read the article below:

Middle-of-funnel content

At this next stage, your target audience is now aware of your business and the solution you provide, and they’re interested.

They trust you enough to make slightly bigger commitments, like signing up for a webinar and engaging with longer-form content; and they’re interested enough in your offering to read brand-specific material like case studies, testimonials, and newsletter emails.

Their goal is to learn more about the solution you provide—both in general and the one specifically offered by your business.

Your goal is to educate them on your offering and set your business apart from competitors—not just with your features and benefits but with your expertise and knowledge.

Next, write a summary of what it says about Middle-of-funnel content
Response: Middle-of-funnel content includes brand-specific content that educates interested audiences that are already considering an offer.

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.

You are a researcher working on developing new text summarization algorithms. Provide an overview of some of the current state-of-the-art approaches in text summarization, focusing on deep learning and neural network-based methods. Explain how these approaches work, including the key components and architectures used. Discuss the advantages and limitations of these modern techniques compared to traditional methods. Your overview should be technically detailed but still accessible to readers with a basic understanding of machine learning concepts.
user
Recent advances in deep learning and neural networks have led to significant progress in text summarization. Many state-of-the-art approaches use encoder-decoder architectures, where the encoder processes the input text to create a semantic representation, and the decoder generates the summary based on this representation. Popular encoder models include recurrent neural networks (RNNs) like LSTMs and transformers like BERT. The decoder often uses an attention mechanism to selectively focus on relevant parts of the input. Abstractive summarization models like Pointer-Generator Networks and Bottom-Up Summarization use a hybrid approach, allowing them to both generate new words and copy from the input. These neural methods can capture complex semantic relationships and generate more coherent summaries. However, they require large training datasets and can struggle with factual consistency and summarizing long documents. Extractive methods based on neural ranking and classification are also being explored for improved informativeness and flexibility.
assistant