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Abstract
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.
Highlights
The increasing amount of data on the Internet today has led to various trends towards automatic text summarization tools
Due to the difficulty associated with the generation of abstracts, most text summarization techniques only focus on the first type
The summary that obtained from our model has the properties of abstractive summary and, as mentioned in section 1, there is no previous work in Arabic abstractive text summarization
Summary
Abstract—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. 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. The proposed model is based on textual graph to remove multi-document redundancy and generate coherent summary. The original text, highly redundant and related multidocument, will be converted into textual graph.
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