Summarize the key points of the following text about summarization techniques in one or two concise paragraphs:

Summarization is the process of condensing a text into a concise version that captures the most important information. There are two main approaches to summarization: extractive and abstractive. Extractive summarization involves identifying and extracting the most salient sentences or phrases from the original text and reproducing them verbatim in a shorter form. Abstractive summarization, on the other hand, involves understanding the semantic meaning of the text and generating a new, condensed representation in natural language. Common techniques for extractive summarization include statistical methods like term frequency-inverse document frequency (TF-IDF) for identifying key concepts and sentences, as well as graph-based methods that rank sentences based on properties like centrality in the text graph. For abstractive summarization, neural sequence-to-sequence models are often used, with an encoder that reads the text and a decoder that generates the summary. Evaluation metrics like ROUGE compare system summaries to human-written reference summaries based on overlap of n-grams.
Summarize the key points of the following text about summarization techniques in one or two concise paragraphs:

Summarization is the process of condensing a text into a concise version that captures the most important information. There are two main approaches to summarization: extractive and abstractive. Extractive summarization involves identifying and extracting the most salient sentences or phrases from the original text and reproducing them verbatim in a shorter form. Abstractive summarization, on the other hand, involves understanding the semantic meaning of the text and generating a new, condensed representation in natural language. Common techniques for extractive summarization include statistical methods like term frequency-inverse document frequency (TF-IDF) for identifying key concepts and sentences, as well as graph-based methods that rank sentences based on properties like centrality in the text graph. For abstractive summarization, neural sequence-to-sequence models are often used, with an encoder that reads the text and a decoder that generates the summary. Evaluation metrics like ROUGE compare system summaries to human-written reference summaries based on overlap of n-grams.
user
The key points regarding summarization techniques are: 1) Summarization aims to condense a text into a concise version capturing the most important information. 2) There are two main approaches - extractive summarization which extracts and reproduces salient sentences verbatim, and abstractive summarization which involves understanding the meaning to generate a new condensed representation. 3) Techniques for extractive summarization include statistical and graph-based methods, while abstractive summarization often uses neural sequence-to-sequence models. 4) Evaluation metrics like ROUGE measure the overlap between system and human-written reference summaries.
assistant
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: Summarize the main points of this news article for me in a couple of sentences.

Oakland Athletics broadcaster Glen Kuiper apologized after uttering what sounded like a racial slur while describing a trip to the Negro Leagues Baseball Museum.

In a pregame segment on NBC Sports California, before the A’s played the Kansas City Royals on Friday night, Kuiper talked about a trip to the museum with colleague Dallas Braden but seemingly mispronounced the word “negro,” making it sound instead like a slur.

Later in the game, Kuiper apologized on the air without getting into specifics.
Response: Glen Kuiper, who is the Oakland Athletics broadcaster, apologized for saying a racial slur.  Kuiper mispronounced "negro" when describing a recent visit to the Negro Leagues Baseball Museum. 

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.
