A Proposed Textual Graph Based Model for Arabic Multi-document Summarization

Text summarization task is still an active area of research in natural language preprocessing. Several methods that have been proposed in the literature to solve this task

have presented mixed success. However, such methods developed

in a multi-document Arabic text summarization are based on

extractive summary and none of them is oriented to abstractive

summary. This is due to the challenges of Arabic language and

lack of resources. In this paper, we present a minimal languagedependent

processing abstractive Arabic multi-document summarizer.

The proposed model is based on textual graph to remove

multi-document redundancy and generate coherent summary.

Firstly, the original text, highly redundant and related multidocument,

will be converted into textual graph. Next, graph

traversal with structural rules will be applied to concatenate

related sentences to single ones. Finally, unwanted and less

weighted phrases will be removed from the summarized sentences

to generate final summary. Preliminary results show that the

proposed method has achieved promising results for multidocument

summarization.