Summarize the key points about abstractive and extractive summarization methods discussed in the following text:

Summarization methods can be broadly classified into two categories: extractive and abstractive.

Extractive summarization methods select and concatenate the most important sentences or phrases from the original text to form the summary. These methods rely on techniques like scoring sentences based on features like word frequency, position, and cue phrases, and then extracting the top-scoring sentences. Some popular extractive methods include TextRank, LexRank, and GreedySum.

On the other hand, abstractive summarization methods generate new sentences that capture the essence of the text, potentially rephrasing and condensing the information. These methods often leverage sequence-to-sequence (seq2seq) models, which take the input text as a sequence and generate the summary as another sequence. Attention mechanisms and copy mechanisms help the model focus on important parts of the input and selectively copy words or phrases. Some well-known abstractive models include Pointer-Generator Networks, Bottom-Up Summarization, and Pre-trained Language Models like BART and T5.

While extractive methods are generally simpler and faster, they may lack coherence and fluency due to the extracted sentences being out of context. Abstractive methods can produce more coherent and concise summaries, but they are more computationally expensive and require large amounts of training data. The choice between the two approaches depends on factors like the desired level of compression, the available resources, and the trade-off between summary quality and efficiency.
Summarize the key points about abstractive and extractive summarization methods discussed in the following text:

Summarization methods can be broadly classified into two categories: extractive and abstractive.

Extractive summarization methods select and concatenate the most important sentences or phrases from the original text to form the summary. These methods rely on techniques like scoring sentences based on features like word frequency, position, and cue phrases, and then extracting the top-scoring sentences. Some popular extractive methods include TextRank, LexRank, and GreedySum.

On the other hand, abstractive summarization methods generate new sentences that capture the essence of the text, potentially rephrasing and condensing the information. These methods often leverage sequence-to-sequence (seq2seq) models, which take the input text as a sequence and generate the summary as another sequence. Attention mechanisms and copy mechanisms help the model focus on important parts of the input and selectively copy words or phrases. Some well-known abstractive models include Pointer-Generator Networks, Bottom-Up Summarization, and Pre-trained Language Models like BART and T5.

While extractive methods are generally simpler and faster, they may lack coherence and fluency due to the extracted sentences being out of context. Abstractive methods can produce more coherent and concise summaries, but they are more computationally expensive and require large amounts of training data. The choice between the two approaches depends on factors like the desired level of compression, the available resources, and the trade-off between summary quality and efficiency.
user
The key points about abstractive and extractive summarization methods are: 1) Extractive methods select and concatenate the most important sentences or phrases from the original text, using techniques like sentence scoring based on features like word frequency, position, and cue phrases. 2) Abstractive methods generate new sentences capturing the essence of the text, often using sequence-to-sequence models with attention and copy mechanisms. 3) Popular extractive methods include TextRank, LexRank, and GreedySum, while abstractive models like Pointer-Generator Networks, Bottom-Up Summarization, and pre-trained language models like BART and T5 are widely used. 4) Extractive methods are simpler and faster but may lack coherence, while abstractive methods can produce more coherent and concise summaries but are computationally expensive. 5) The choice depends on factors like desired compression level, available resources, and the trade-off between summary quality and efficiency.
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: Could you summarize what this article says are ways to use essential oils? The article says:
AROMA DIFFUSER
This method is beneficial for mood enhancement and upliftment.  It disinfects the air and can boost immunity through inhalation.  Fill the bowl with water, add 5-15 drops and enjoy
STEAM INHALATION
This method is used mainly for upper respiratory problems such as bronchial and sinus congestion, colds, coughs, and influenza.  Fill a large bowl with boiling water and add three drops of essential oil.  Lean over the bowl with your eyes closed and a towel over your head and inhale the steam for 5-10 minutes.
VAPORIZER
An effective decongesting method for respiratory conditions.  Add from 3 to 9 drops of essential oil to the water of the vaporizer.  Run overnight.
Response: Three ways to use essential oils include in an aroma diffuser, via steam inhalation, and in a vaporizer. 

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.
