SemG-TS: Abstractive Arabic Text Summarization Using Semantic Graph Embedding

This study proposes a novel semantic graph embedding-based abstractive text summarization technique for the Arabic language, namely SemG-TS. SemG-TS employs deep neural network to produce summary. A set of experiments were conducted evaluate performance and compare results those popular baseline word embedding called word2vec. new dataset was collected experiments. Two evaluation methodologies followed in experiments: automatic human evaluations. The Rouge measure used evaluation, while native speakers tasked relevancy, similarity, readability, overall satisfaction generated summaries. obtained prove superiority