Summarize the following set of instructions on how to perform keyword extraction, a crucial task in text summarization:

Keyword extraction is a fundamental technique in text summarization that involves identifying the most significant words or phrases in a document. These keywords serve as a condensed representation of the text's main topics and concepts. Follow these steps to perform keyword extraction:

1. Pre-process the text: Remove irrelevant elements such as stop words, punctuation, and HTML tags, and convert all words to lowercase.

2. Tokenize the text: Split the text into individual words or sequences of words (n-grams) that will be considered as potential keywords.

3. Calculate word frequencies: Count the occurrences of each word or n-gram in the text. Words with higher frequencies are more likely to be important keywords.

4. Apply term weighting: Compute a weight or score for each word or n-gram based on its frequency, position in the text, or other statistical measures like TF-IDF (Term Frequency-Inverse Document Frequency).

5. Filter and rank keywords: Set a threshold to filter out low-scoring words, and rank the remaining words based on their weights or scores.

6. (Optional) Use additional features: Incorporate linguistic features like part-of-speech tags, named entity recognition, or word embeddings to improve keyword extraction accuracy.

7. (Optional) Employ machine learning models: Train supervised or unsupervised machine learning models on labeled data to learn patterns and automatically extract keywords from new texts.

8. Post-process and refine: Remove redundant or overlapping keywords, and optionally group related keywords into keyphrases for better coherence and readability.
Summarize the following set of instructions on how to perform keyword extraction, a crucial task in text summarization:

Keyword extraction is a fundamental technique in text summarization that involves identifying the most significant words or phrases in a document. These keywords serve as a condensed representation of the text's main topics and concepts. Follow these steps to perform keyword extraction:

1. Pre-process the text: Remove irrelevant elements such as stop words, punctuation, and HTML tags, and convert all words to lowercase.

2. Tokenize the text: Split the text into individual words or sequences of words (n-grams) that will be considered as potential keywords.

3. Calculate word frequencies: Count the occurrences of each word or n-gram in the text. Words with higher frequencies are more likely to be important keywords.

4. Apply term weighting: Compute a weight or score for each word or n-gram based on its frequency, position in the text, or other statistical measures like TF-IDF (Term Frequency-Inverse Document Frequency).

5. Filter and rank keywords: Set a threshold to filter out low-scoring words, and rank the remaining words based on their weights or scores.

6. (Optional) Use additional features: Incorporate linguistic features like part-of-speech tags, named entity recognition, or word embeddings to improve keyword extraction accuracy.

7. (Optional) Employ machine learning models: Train supervised or unsupervised machine learning models on labeled data to learn patterns and automatically extract keywords from new texts.

8. Post-process and refine: Remove redundant or overlapping keywords, and optionally group related keywords into keyphrases for better coherence and readability.
user
Keyword extraction involves identifying the most significant words or phrases in a text to represent its main topics and concepts. It involves pre-processing and tokenizing the text, calculating word frequencies or term weights, filtering and ranking keywords based on scores, and optionally using linguistic features or machine learning models. Post-processing may include removing redundancy and grouping related keywords into keyphrases.
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: Tell me, briefly, what this book description says about Native American women.

In the summer of 2017, twenty-two-year-old Savanna LaFontaine-Greywind vanished. A week after she disappeared, police arrested the white couple who lived upstairs from Savanna and emerged from their apartment carrying an infant girl. The baby was Savanna’s, but Savanna’s body would not be found for days.

The horrifying crime sent shock waves far beyond Fargo, North Dakota, where it occurred, and helped expose the sexual and physical violence Native American women and girls have endured since the country’s colonization.

With pathos and compassion, Searching for Savanna confronts this history of dehumanization toward Indigenous women and the government’s complicity in the crisis. Featuring in-depth interviews, personal accounts, and trial analysis, Searching for Savanna investigates these injustices and the decades-long struggle by Native American advocates for meaningful change.
Response: Native American women have been subjected to sexual and physical violence and dehumanization since the country's colonization.

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.
