Summarize the different summarization approaches and models mentioned in the following passage:

There are several approaches and models used for automatic text summarization:

1. Statistical/unsupervised approaches like TextRank and LexRank score sentences based on graphs capturing word/sentence relationships.

2. Supervised machine learning models are trained on document-summary pairs to learn what content to include. Common architectures include SVMs, logistic regression, and neural sequence models.

3. Sequence-to-sequence (seq2seq) models with encoder-decoder architectures like LSTM-based models generate abstractive summaries.

4. Transformer-based pretrained language models like BERT, RoBERTa, and BART can be fine-tuned for summarization.

5. Graph neural networks like GraphSum operate on document graphs capturing semantic relationships between sentences.

6. Bottom-up and top-down summarization models iteratively refine sentence or content selection.

7. Multi-task models combine summarization with related tasks like question answering and entailment.

8. Reinforcement learning approaches directly optimize summary quality using rewards from evaluation metrics.
Summarize the different summarization approaches and models mentioned in the following passage:

There are several approaches and models used for automatic text summarization:

1. Statistical/unsupervised approaches like TextRank and LexRank score sentences based on graphs capturing word/sentence relationships.

2. Supervised machine learning models are trained on document-summary pairs to learn what content to include. Common architectures include SVMs, logistic regression, and neural sequence models.

3. Sequence-to-sequence (seq2seq) models with encoder-decoder architectures like LSTM-based models generate abstractive summaries.

4. Transformer-based pretrained language models like BERT, RoBERTa, and BART can be fine-tuned for summarization.

5. Graph neural networks like GraphSum operate on document graphs capturing semantic relationships between sentences.

6. Bottom-up and top-down summarization models iteratively refine sentence or content selection.

7. Multi-task models combine summarization with related tasks like question answering and entailment.

8. Reinforcement learning approaches directly optimize summary quality using rewards from evaluation metrics.
user
The summarization approaches and models mentioned include: 1) Statistical/unsupervised methods like TextRank and LexRank, 2) Supervised machine learning models, 3) Sequence-to-sequence neural models, 4) Pretrained transformer language models, 5) Graph neural networks, 6) Bottom-up and top-down iterative models, 7) Multi-task models combined with related tasks, 8) Reinforcement learning optimizing evaluation metrics as rewards.
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.
