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Text Summarization

Generating concise summaries of longer texts, either extractive (selecting sentences) or abstractive (generating new text).

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4 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  3. Recurrent Neural Network ✓ understood

    A neural network architecture with loops that allow information to persist, designed for sequential data like text and time series.

  4. Sequence-to-Sequence ✓ understood

    Models that transform input sequences to output sequences, used for translation, summarization, and generation.

  5. Text Summarization · you are here ✓ understood

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Sequence-to-Sequence
Text Summarization

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ROUGE Score

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Evaluation ROUGE Score Metrics for evaluating text summarization by measuring overlap of n-grams, word sequences, and word pairs with references. Language & LLMs Text Generation Automatically creating coherent text using language models, from simple completion to creative writing. Language & LLMs Large Language Model A neural network, almost always a transformer, trained on vast amounts of text to predict the next token, which lets it write, answer, summarize and follow instructions. Language & LLMs Information Extraction Automatically extracting structured information from unstructured text. Language & LLMs BART Bidirectional and Auto-Regressive Transformer - combines BERT-like encoder with GPT-like decoder for sequence-to-sequence tasks.

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2 papers that build on Text Summarization .