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 future directions and research challenges in text summarization mentioned in the following text:

Despite the significant progress made in text summarization over the past decades, several challenges and future research directions remain to be addressed.

One area of ongoing research is the development of more advanced abstractive summarization techniques that can truly capture the semantic essence of the text and generate coherent, fluent, and informative summaries. This may involve the integration of commonsense reasoning, discourse understanding, and knowledge grounding to enable more human-like summarization capabilities.

Another challenge is the adaptation of summarization systems to handle diverse genres and domains of text, such as scientific literature, legal documents, social media content, and multimedia data (e.g., summarizing videos or multi-modal information). This requires the development of domain-aware and context-sensitive summarization models that can effectively capture and represent the nuances and conventions of different text types.

Personalized and query-focused summarization is another area of interest, where summaries are tailored to the specific needs and interests of individual users or generated in response to specific queries or information needs. This involves incorporating user modeling, context understanding, and query analysis techniques into the summarization process.

Additionally, the scalability and efficiency of summarization systems remain important considerations, especially in scenarios where large volumes of data need to be processed in near real-time, such as in news aggregation or social media monitoring applications.

Furthermore, there is a growing interest in exploring the ethical implications of summarization systems, such as potential biases, privacy concerns, and the responsible use of these technologies in high-stakes decision-making scenarios.

Overall, the future of text summarization research lies in developing more intelligent, domain-adaptive, personalized, and responsible summarization systems that can effectively distill and communicate the most relevant information from diverse and ever-growing data sources.
user
The text discusses future directions and research challenges in text summarization, including developing advanced abstractive techniques for generating coherent and informative summaries through commonsense reasoning and discourse understanding; adapting systems to handle diverse genres and domains like scientific literature, legal texts, social media, and multimedia data; enabling personalized and query-focused summarization tailored to user needs and information requirements; improving scalability and efficiency for processing large volumes of data in near real-time; and exploring ethical considerations such as biases, privacy, and responsible use in high-stakes decision-making scenarios. Overall, the goal is to develop more intelligent, domain-adaptive, personalized, and responsible summarization systems that can effectively communicate relevant information from diverse data sources.
assistant