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.

Please read the following passage and summarize the key points regarding summarization techniques:

Summarization is the process of condensing a large piece of information into its most important or relevant elements. There are two main approaches to summarization: extractive and abstractive. Extractive summarization selects important sentences or phrases from the original text and combines them to form a concise summary. Abstractive summarization, on the other hand, aims to generate entirely new sentences that capture the essence of the source material. This approach involves a deeper understanding of the content and requires more advanced natural language processing techniques.

Popular extractive summarization methods include term frequency-inverse document frequency (TF-IDF), TextRank, and Latent Semantic Analysis (LSA). TF-IDF identifies important words or phrases based on their frequency and uniqueness within the text. TextRank applies a graph-based ranking algorithm to determine the most important sentences. LSA uses vector space modeling to identify the underlying concepts and select representative sentences.

Abstractive summarization often relies on sequence-to-sequence models, such as recurrent neural networks (RNNs) or transformers like BERT. These models learn to generate summaries by training on large datasets of text and corresponding human-written summaries. Recent advancements in transfer learning and pre-trained language models have significantly improved the quality of abstractive summaries.
user
The passage discusses two main techniques for summarization: extractive and abstractive. Extractive summarization selects and combines important sentences or phrases from the original text, using methods like TF-IDF, TextRank, and LSA. Abstractive summarization generates entirely new sentences that capture the essence of the source material, often using advanced neural networks like RNNs or transformers trained on large datasets.
assistant