The "Arabic Text Summarization with AraBERT" project aimed to harness the power of AraBERT, a transformer-based model, for summarizing Arabic language text. AraBERT was fine-tuned on an Arabic summarization dataset to produce concise and coherent summaries.
- Explore AraBERT Architecture: Investigate the architecture of AraBERT and its applicability to Arabic text summarization.
- Fine-Tuning Process: Describe the process of fine-tuning AraBERT on an Arabic summarization dataset.
- Performance Evaluation: Evaluate the performance of the fine-tuned model on a validation dataset.
- Case Studies: Present case studies showcasing the model's effectiveness in summarizing diverse Arabic text.
ROUGE is a set of metrics used for evaluating the quality of summaries by comparing them to reference summaries. It includes measures such as ROUGE-N (unigrams, bigrams, etc.), ROUGE-L (longest common subsequence), and ROUGE-W (weighted longest common subsequence). These metrics provide a quantitative assessment of the overlap and content similarity between generated and reference summaries.
AraBERT is a transformer-based model introduced for processing Arabic language text. It has been fine-tuned for various natural language processing tasks, including summarization, by leveraging transfer learning.
The project utilized the csebuetnlp/xlsum dataset for Arabic summarization. This dataset consists of a variety of documents with corresponding summaries.
Data preprocessing involved tokenization using the AraBERT tokenizer, handling input-output sequences, and ensuring the proper format for training.
AraBERT was fine-tuned on the Arabic summarization dataset using a sequence-to-sequence training approach.
ROUGE Scores
The model's performance was evaluated using various ROUGE metrics, including ROUGE-N, ROUGE-L, and ROUGE-W. The following table presents the key ROUGE scores:
Metric | Score |
---|---|
ROUGE-1 | 2.3202 |
ROUGE-2 | 0.0977 |
ROUGE-L | 2.3113 |