Model Pipeline - Abstractive summarization
Abstractive summarization creates a short, new summary of a long text by understanding its meaning, like telling a friend what a story is about in your own words.
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Abstractive summarization in NLP - Model Pipeline Trace
Data Flow - 5 Stages
1Input Text
1 document x 500 words→Raw text input for summarization→1 document x 500 words
"The quick brown fox jumps over the lazy dog near the river bank on a sunny day..."
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2Text Preprocessing
1 document x 500 words→Clean text, remove punctuation, tokenize words→1 document x 480 tokens
["quick", "brown", "fox", "jumps", "lazy", "dog", "river", "bank", "sunny", "day"]
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3Feature Engineering
1 document x 480 tokens→Convert tokens to embeddings (numbers representing words)→1 document x 480 tokens x 768 features
[[0.12, -0.34, ..., 0.56], [0.45, 0.67, ..., -0.23], ...]
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4Model Training
1 document x 480 tokens x 768 features→Train sequence-to-sequence model to generate summary→1 summary x 50 tokens
"A fox jumps over a dog near a river on a sunny day."
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5Evaluation Metrics
Generated summary and reference summary→Calculate ROUGE scores to measure quality→ROUGE-1: 0.45, ROUGE-2: 0.30, ROUGE-L: 0.40
ROUGE-1 compares overlapping words between generated and reference summaries
Training Trace - Epoch by Epoch
Loss
2.3 |****
2.0 |***
1.5 |**
1.0 |*
0.5 |
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1 2 3 4 5 Epochs| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 2.30 | 0.20 | Model starts learning, loss high, accuracy low |
| 2 | 1.85 | 0.35 | Loss decreases, model improves summary generation |
| 3 | 1.50 | 0.50 | Better understanding of text, summaries more accurate |
| 4 | 1.20 | 0.60 | Model converging, summaries coherent and concise |
| 5 | 1.00 | 0.68 | Training stabilizes, good balance of loss and accuracy |
Prediction Trace - 5 Layers
Layer 1: Input Embedding Layer
Layer 2: Encoder (Transformer Layers)
Layer 3: Decoder (Transformer Layers)
Layer 4: Softmax Layer
Layer 5: Output Summary
Model Quiz - 3 Questions
Test your understanding
What does the encoder part of the model do in abstractive summarization?
Key Insight
Abstractive summarization models learn to understand the meaning of long texts and generate new, shorter summaries by training on many examples. Watching loss decrease and accuracy increase shows the model is improving its ability to create meaningful summaries.