Reference 7 stops to get here

ROUGE Score

Metrics for evaluating text summarization by measuring overlap of n-grams, word sequences, and word pairs with references.

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7 stops · basics first
  1. Token ✓ understood

    The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output.

  2. N-gram ✓ understood

    A contiguous sequence of n items (words, characters) from text, used in language modeling and feature extraction.

  3. 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.

  4. 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.

  5. Recurrent Neural Network ✓ understood

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

  6. Sequence-to-Sequence ✓ understood

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

  7. Text Summarization ✓ understood

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

  8. ROUGE Score · you are here ✓ understood

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ROUGE Score

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Evaluation BLEU Score A metric for evaluating machine translation quality by comparing n-gram overlap between generated and reference text. Evaluation Perplexity A metric measuring how well a language model predicts text - lower perplexity indicates better prediction. Evaluation Benchmark A standardized dataset and task used to compare model performance across different approaches (ImageNet, GLUE, SuperGLUE). Evaluation Precision The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive. Evaluation Recall The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.