TY  - JOUR
T1  - Automatic Summarization Arabic Text Using Key Phrases Extraction
AU - Noori Feje, Hamzah AU - Ajmi Falih, Mohanaed 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 6
SP  - 1395
EP  - 1399
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.1395.1399
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.1395.1399
KW  - Text summarization
KW  -key phrase extraction
KW  -similarity
KW  -ROUGE matrix
KW  -techniques
KW  -rapid
KW  -single-document
AB  - Because of the growing number of electronic documents, human being are badly in need of more
rapid techniques for evaluating the link of documents. Summarization is representation of underlying written
text. A full underst anding of the document is essential to form an ideal summary. However, achieving full
underst anding is either difficult or impossible for computers. Therefore, selecting main sentences from the
original text and introducing these sentences as a summary present the most frequent techniques in automated
text summarization. This study propose using key phrase extraction module is applied to extract main important
key phrases from the text that helps specify the most important sentences and find similar sentences based on
similarity algorithm. It is applicable to extract one sentence from a set of similar sentences while overcoming the
other similar sentences (i.e., sentences that have a greater similarity than the predefined threshold). This model
is designed for single-document Arabic text summarization. The Recall-Oriented Understudy for Gisting
Evaluation (ROUGE) matrix is employed for the assessment. For the summarization dataset, Essex Arabic
Summaries Corpus was used. It has many topic based articles with multiple human summaries. This model
achieved accuracy more than 80%.
ER  -