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: Shorten the following text to less than 50 words: 

Plant oils have been used medicinally for over 2,500 years. The first references to the use of plant oils can be traced to Chinese medicine, also known as Traditional Chinese Medicine.

Various parts of plants were consumed in either the raw state or dried, boiled, or steamed (to extract the oil). The end product was then consumed, inserted into any one of the natural openings of the body, massaged into the skin, or inhaled in vapor form.

During the Medieval period, a collection of medicinal treatments for a variety of ailments was published by Abu Ali al-Husain Ebn Abdullah Ebn Sina (980–1037 AD). He was known in the West as Avicenna, a Persian physician who was also known as one of the most influential medical writers in the medieval period.

Avicenna`s Canon of Medicine, arguably one of the most famous books in the history of medicine, presents a clear and organized summary of all of the medical knowledge of the time, including a long list of drugs. Interestingly, lily and spotted orchid oils are mentioned as remedies for tinnitus (Mahdizadeh et al, 2015).

Throughout the 18th and 19th centuries, advances in western medicine progressed. The American Medical Association was founded in 1847 and the American Pharmaceutical Association was founded in 1852. At the federal level, the Food, Drug, and Insecticide Administration (FDIA) was created 1927 and, three years later, changed its name to the Food and Drug Administration (FDA).
Response: Plant oils have been used for a long time. Plants in general have been used in many forms and in many ways, with publications about this ranging from Traditional Chinese Medicine to the Medieval period. Since then, Western medicine has progressed, with organizations like the FDA being founded. 

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.

Summarize the different types of summarization techniques and approaches mentioned in the following text:

There are various techniques and approaches to text summarization, each with its own strengths and limitations. One broad categorization is extractive versus abstractive summarization.

Extractive summarization involves identifying and extracting the most relevant sentences or phrases from the original text and concatenating them to form the summary. This approach is relatively straightforward and can be effective for certain types of text, but it may result in incoherent or redundant summaries, as the extracted sentences are taken out of context.

Abstractive summarization, on the other hand, aims to generate entirely new sentences that capture the essence of the original text. This approach often involves techniques from natural language generation and can produce more coherent and concise summaries. However, it is a more complex task that requires a deeper understanding of the text's semantics and discourse structure.

Within these broad categories, there are various specific techniques, such as topic modeling, graph-based methods, sequence-to-sequence models, and attention mechanisms. Topic modeling approaches identify and extract sentences based on their relevance to the main topics discussed in the text. Graph-based methods represent the text as a graph, where nodes represent concepts or sentences, and edges represent their relationships, allowing for the identification of central and well-connected elements.

Sequence-to-sequence models, commonly used in machine translation, have also been adapted for summarization tasks. These models learn to map the input sequence (the original text) to an output sequence (the summary) through an encoder-decoder architecture. Attention mechanisms further enhance these models by allowing them to focus on the most relevant parts of the input when generating each part of the output summary.

Other approaches include rule-based systems, which rely on manually crafted rules and heuristics, and hybrid methods that combine multiple techniques to leverage their respective strengths.
user
The text discusses various techniques and approaches to text summarization, broadly categorized as extractive and abstractive. Extractive summarization involves identifying and extracting relevant sentences or phrases from the original text and concatenating them, while abstractive summarization aims to generate entirely new sentences capturing the essence. Specific techniques mentioned include topic modeling, graph-based methods, sequence-to-sequence models with attention mechanisms, rule-based systems, and hybrid approaches combining multiple techniques. Extractive methods are relatively straightforward but may result in incoherent or redundant summaries, while abstractive methods are more complex but can produce more coherent and concise summaries.
assistant