A Robust Approach to Fine-tune Pre-trained Transformer-based models for Text Summarization through Latent Space Compression
Bibliographic record
Abstract
We proposed a technique to reduce the decoder’s number of parameters in a sequence-to-sequence (seq2seq) architecture for automatic text summarization. This approach uses a pre-trained Autoencoder (AE) trained on top of an encoder’s output to reduce its embedding dimension, which significantly reduces the summarizer model’s decoder size. Two experiments were performed to validate the idea: a custom seq2seq architecture with various pre-trained encoders and incorporating the approach in an encoder-decoder model (BART) for text summarization. Both studies showed promising results in terms of ROUGE score. However, the impressive outcome is the 54% decrease in the inference time and a 57% drop in GPU memory usage while fine-tuning with minimal quality loss (4.5% R1 score). It significantly reduces the hardware requirement to fine-tune large-scale pre-trained models. It is also shown that our approach can be combined with other network size reduction techniques (e.g. Distillation) to further reduce any encoder-decoder model parameters count. The implementation and checkpoints are available on GitHub.1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".