BERT Fine-tuning For Arabic Text Summarization
This work addresses the lack of summarization models for Arabic, but it is incremental as it applies an existing fine-tuning method to a new language.
The paper tackled the problem of text summarization for Arabic by fine-tuning a multilingual BERT model, achieving the first documented model for abstractive Arabic summarization and demonstrating its performance in both extractive and abstractive tasks.
Fine-tuning a pretrained BERT model is the state of the art method for extractive/abstractive text summarization, in this paper we showcase how this fine-tuning method can be applied to the Arabic language to both construct the first documented model for abstractive Arabic text summarization and show its performance in Arabic extractive summarization. Our model works with multilingual BERT (as Arabic language does not have a pretrained BERT of its own). We show its performance in English corpus first before applying it to Arabic corpora in both extractive and abstractive tasks.