Arabic Abstractive Text Summarization Using Multilingual T5
Fine-tuned Multilingual T5 (mT5) architectures on complex Arabic text corpora, outperforming traditional seq2seq baselines across ROUGE-1/2/L.
The situation & problem
Arabic natural language processing faces extreme morphological variation, clitic attachment, and sparse gold-standard abstractive training sets.
The operational constraints
Mitigating catastrophic forgetting while fine-tuning heavy multilingual foundation models on domain-specific Arabic journalistic and academic corpora.
Architectural tradeoffs & what was refused
Simple extractive frequency ranking masquerading as abstractive synthesis. Translating Arabic to English, summarizing in English, and back-translating.
What I built & shipped
Optimized fine-tuning pipeline for mT5 with customized Arabic tokenization, dynamic length penalty scheduling, and automated ROUGE evaluation harness.
Architecture & stack
Python, PyTorch, Hugging Face Transformers, mT5, SentencePiece, Accelerate.
Result & measurable impact
Statistically validated improvement in ROUGE-1, ROUGE-2, and ROUGE-L over existing recurrent and vanilla transformer baselines.
Next iterations & roadmap
Distillation into lightweight 4-bit quantized models for edge and client-side web browser inference.
System surfaces & interface captures
Hover and click any record to open the full-resolution pixel inspector with zoom lens and 1:1 precision.
Have a similar constraint?
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