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: Please read this text: 

People consume information in lots of different ways: visually, audibly, through text, and sometimes all three at once. Search engine algorithms like Google’s are advancing to reflect this experience for users. No longer will the algorithms just look at web pages as a single piece of text content. Google will look at all the ways real people consume information: text, images, videos, infographics, tables, charts, and interactive elements that make up a piece of content.

In recent years, we saw the rise of visual search – particularly in image-heavy industries like travel and tourism or ecommerce. Moving into 2023, Google will expand its foothold in multisearch and multisearch near me. 

What does it say about multi-format content in a brief summary?
Response: The text says that people prefer content in different forms like audio, video, or text format. As a result, Google plans to advance the multi-search function and page search algorithms accordingly. 

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 key techniques and applications mentioned in the following text related to text summarization:

Text summarization techniques have evolved significantly in recent years, driven by advances in natural language processing and machine learning. Traditional methods, such as extractive summarization, rely on statistical and linguistic features to identify and extract the most relevant sentences or phrases from the original text to form a summary. However, more advanced techniques, such as abstractive summarization, have emerged to generate entirely new sentences that capture the essence of the source text.

Abstractive summarization often employs deep learning models, such as sequence-to-sequence models, transformer architectures, and reinforcement learning techniques, to interpret and rephrase the content while preserving its core meaning. These models are trained on large datasets to learn the patterns and nuances of language, enabling them to generate coherent and fluent summaries.

Text summarization has numerous applications across various domains, including news summarization, scientific literature summarization, document summarization for legal and financial sectors, and summarization of customer reviews and feedback. Additionally, summarization techniques are increasingly being integrated into virtual assistants, search engines, and content recommendation systems to provide users with concise and informative summaries of relevant information.
user
The key text summarization techniques and applications mentioned in the given text are: 1) Extractive summarization methods that rely on statistical and linguistic features to identify and extract relevant sentences or phrases from the original text. 2) Abstractive summarization techniques that employ deep learning models, such as sequence-to-sequence models, transformer architectures, and reinforcement learning, to generate new sentences capturing the essence of the source text. 3) Applications include news summarization, scientific literature summarization, document summarization for legal and financial sectors, customer review summarization, and integration into virtual assistants, search engines, and content recommendation systems.
assistant