BERT Fine-tuning For Arabic Text Summarization
Computation and Language
2020-04-30 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.2004.14135,
title = {BERT Fine-tuning For Arabic Text Summarization},
author = {Khalid N. Elmadani and Mukhtar Elgezouli and Anas Showk},
journal= {arXiv preprint arXiv:2004.14135},
year = {2020}
}
Comments
4 pages, 2 tables, Published as a conference paper at AfricaNLP workshop at ICLR 2020