nlpyang/BertSum

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1506 stars · 410 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BertSum is the official PyTorch implementation of the paper 'Fine-tune BERT for Extractive Summarization', providing preprocessing pipelines and training code for BERT-based extractive summarization models. It includes data preparation for CNN/DailyMail, multiple summarization head architectures (classifier, Transformer, LSTM), and pretrained model results.

Use cases

• fine-tune BERT for extractive text summarization
• reproduce ROUGE benchmark results on CNN/DailyMail
• train a sentence-level summarization classifier
• preprocess news articles for summarization research
• compare summarization model architectures like Transformer and LSTM heads

When to choose

• you need a research-grade extractive summarization baseline with published ROUGE scores
• you want to fine-tune BERT specifically for sentence selection in summarization
• you are working with the CNN/DailyMail dataset and need preprocessing tooling

When to avoid

• you need abstractive summarization rather than extractive
• you want a production-ready, actively maintained library with modern dependency versions
• you need support for newer transformer models beyond BERT or easy installation

Facets

library · maturity maintenance

machine-learning deep-learning nlp llm-training deep-learning machine-learning python extractive-summarization bert pytorch research-code text-summarization natural-language-processing linux

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