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/* Creating a text summarization program in Python within Jupyter Notebook involves using libraries like NLTK, spaCy, or transformers (for advanced models like BERT). Here's a step-by-step guide to build a basic text summarizer: Steps: 1. Install Required Libraries Ensure the following libraries are installed. You can install them using: pip install nltk spacy transformers 2. Choose a Summarization Approach Extractive Summarization: Extracts key sentences from the text. Abstractive Summarization: Generates a concise version of the text using advanced models like BERT. 3. Implementation Below is an example code for both approaches. --- Code 1. Extractive Summarization with NLTK This method selects important sentences from the text based on word frequency. # Import required libraries import nltk from nltk.tokenize import sent_tokenize, word_tokenize from nltk.corpus import stopwords from collections import Counter # Download NLTK data (run only once) nltk.downloa...

/* Creating a text summarization in Python can be done using popular libraries like transformers, spaCy, or gensim. Here's a step-by-step guide to implementing text summarization in a Jupyter notebook: Step 1: Install Necessary Libraries First, ensure you have the required libraries installed. You can use the following commands in your Jupyter notebook: !pip install transformers !pip install torch Step 2: Import Required Libraries Import the necessary modules for text summarization. We'll use the transformers library with a pre-trained model for summarization. from transformers import pipeline Step 3: Load the Pre-Trained Model Initialize a pre-trained summarization pipeline. summarizer = pipeline("summarization") Step 4: Input the Text for Summarization Provide the text you want to summarize. text = """ Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific appl...

Happy coding # Install dependencies in Jupyter Notebook !pip install sumy nltk # Import necessary libraries from sumy.parsers.plaintext import PlaintextParser from sumy.nlp.tokenizers import Tokenizer from sumy.summarizers.lex_rank import LexRankSummarizer # Define summarization function def summarize_text(text: str, sentence_count: int = 5) -> str: parser = PlaintextParser.from_string(text, Tokenizer("english")) summarizer = LexRankSummarizer() summary = summarizer(parser.document, sentence_count) summarized_text = " ".join([str(sentence) for sentence in summary]) return summarized_text # Input text input_text = """ Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. The ideal charact...

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