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: Briefly summarize what this scientific news article says about the phenomenon of gravitational lensing caused by massive cosmic objects like galaxy clusters.

Here's the source text:
The 1915 theory, which is occasionally called Einstein's geometric theory of gravity, predicts that, as bowling balls placed on a trampoline create a depression, objects with mass cause the very fabric of space-time to warp. This curvature gives rise to the force of gravity. And the greater the mass of a cosmic object, the more extreme the warping of space it causes.

Light travels across the universe in straight lines, but when it encounters a warp caused by a truly massive object, its path is curved. When the warping object is between Earth and a background object, it can curve light in such a way that the apparent position of the background object is shifted.

But when the intermediate or "lensing object" is truly massive — like a monstrous cluster of galaxies, for example — light from the background source takes a different amount of time to reach Earth depending on how close it passes to the natural cosmic lens. 

This effect, called gravitational lensing, can make single objects appear at multiple points in the sky, often in stunning arrangements called Einstein rings and Einstein crosses. It can also cause background objects to appear amplified in the sky, a powerful effect that astronomers use to observe distant and early faint galaxies.

The distortion caused by massive clusters like eMACS J1823.1+7822 can also help astronomers study mysterious dark matter, which accounts for around 85% of the mass in the universe but is invisible because it does not interact with electromagnetic radiation. Because dark matter does interact gravitationally, however, the lensing of light by a galaxy or galactic cluster can help researchers map the distribution of dark matter.
Response: This scientific news article discusses gravitational lensing, which is caused by massive cosmic objects and is in line with Einstein's theory of gravity. Objects with high mass can cause the bending of light paths due to the warping of space-time. Gravitational lensing occurs when a large object bends the path of light, causing a shift in the apparent position of a background object. Gravitational lensing creates stunning visual effects such as Einstein rings and crosses. Gravitational lensing helps astronomers see distant and faint galaxies by making them appear larger in the sky. Massive galaxy clusters like eMACS J1823.1+7822 help scientists map the distribution of dark matter, which makes up a significant portion of the universe's mass.

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 following passage discussing the future trends and advancements in text summarization techniques:

As the field of text summarization continues to evolve, several promising trends and advancements are on the horizon. One significant area of development is the integration of multimodal data sources, where summarization systems can process and summarize information from a combination of text, images, videos, and other multimedia formats. This capability will be particularly valuable in domains such as social media analysis, where content often consists of a rich blend of different modalities.

Another trend is the emergence of personalized and context-aware summarization, where summaries are tailored to the specific interests, background knowledge, and preferences of individual users or applications. By leveraging user profiles, contextual information, and adaptive techniques, summarization systems can generate more relevant and customized summaries, enhancing their utility and user experience.

Additionally, the integration of summarization techniques with other natural language processing tasks, such as question answering, dialogue systems, and information retrieval, is gaining traction. This holistic approach enables the development of more sophisticated and intelligent systems that can understand and summarize information in response to specific queries or conversational contexts.

As the field of artificial intelligence and machine learning continues to advance, there will be increased adoption of deep learning models and techniques in text summarization. These models can leverage large-scale datasets and learn complex patterns and representations, potentially enabling more accurate and fluent summaries, even for challenging and specialized domains.

Furthermore, the development of more transparent and interpretable summarization models is a key area of focus. Ensuring that the rationale behind the generated summaries is explainable and trustworthy is crucial for applications in domains such as healthcare, finance, and policymaking, where decisions based on summarized information can have significant consequences.

Overall, the future of text summarization techniques promises to be exciting, with advancements in multimodal processing, personalization, integration with other NLP tasks, deep learning models, and interpretability paving the way for more intelligent, context-aware, and trustworthy summarization systems.
user
The passage discusses future trends and advancements in text summarization techniques. It highlights the integration of multimodal data sources like text, images, and videos for summarization, enabling applications like social media analysis. Personalized and context-aware summarization tailored to individual user preferences and backgrounds is an emerging trend. Summarization is being integrated with other NLP tasks like question answering and dialogue systems for more sophisticated and intelligent systems. Increased adoption of deep learning models and techniques leveraging large datasets is expected, potentially enabling more accurate and fluent summaries. Developing transparent and interpretable summarization models is crucial for applications where trustworthiness is paramount, such as healthcare and finance. Overall, advancements in areas like multimodal processing, personalization, integration with other NLP tasks, deep learning, and interpretability promise more intelligent, context-aware, and trustworthy summarization systems.
assistant