Text Summarization: A Brief Review
作者： Laith Abualigah , Mohammad Qassem Bashabsheh , Hamzeh Alabool , Mohammad Shehab
DOI: 10.1007/978-3-030-34614-0_1
关键词: Domain (software engineering) 、 Information retrieval 、 Field (computer science) 、 Automatic summarization 、 Process (engineering) 、 Computer science
摘要: Text Summarization is the process of creating a summary certain document that contains most important information original one, purpose it to get main points document. Abstractive summarization multi-documents aims generate concentrated version while keeping information. Due massive amount data these days, importance arose. Finally, this paper collects recent and relevant research in field text study analysis for future research. It will be significant by giving new direction who are interested domain future.
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Michael Elhadad, Regina Barzilay, Using lexical chains for text summarization Intelligent Scalable Text Summarization. ,(1997) , 10.7916/D85B09VZ
Horacio Saggion, Automatic Summarization: An Overview Revue française de linguistique appliquée. ,vol. XIII, pp. 63- 81 ,(2008) , 10.3917/RFLA.131.0063
Anna Wilbik, James M. Keller, A Fuzzy Measure Similarity Between Sets of Linguistic Summaries IEEE Transactions on Fuzzy Systems. ,vol. 21, pp. 183- 189 ,(2013) , 10.1109/TFUZZ.2012.2214225
Dragomir R. Radev, Eduard Hovy, Kathleen McKeown, Introduction to the special issue on summarization Computational Linguistics. ,vol. 28, pp. 399- 408 ,(2002) , 10.1162/089120102762671927
Ahmed Ibrahim, Tarek Elghazaly, Improve the Automatic Summarization of Arabic Text Depending on Rhetorical Structure Theory mexican international conference on artificial intelligence. pp. 223- 227 ,(2013) , 10.1109/MICAI.2013.35
Laith Mohammad Qasim Abualigah, Essam S.Hanandeh, APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL International Journal of Computer Science, Engineering and Applications. ,vol. 5, pp. 19- 28 ,(2015) , 10.5121/IJCSEA.2015.5102
Laith Mohammad Abualigah, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Multi-objectives-based text clustering technique using K-mean algorithm international conference on computer science and information technology. pp. 1- 6 ,(2016) , 10.1109/CSIT.2016.7549464
Laith Mohammad Abualigah, Ahamad Tajudin Khader, Mohammed Azmi AlBetar, Essam Said Hanandeh, Unsupervised Text Feature Selection Technique Based on Particle Swarm Optimization Algorithm for Improving the Text Clustering First EAI International Conference on Computer Science and Engineering. pp. 169- 178 ,(2017) , 10.4108/EAI.27-2-2017.152282
Laith Mohammad Abualigah, Ahamad Tajudin Khader, Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering The Journal of Supercomputing. ,vol. 73, pp. 4773- 4795 ,(2017) , 10.1007/S11227-017-2046-2
Laith Mohammad Abualigah, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Osama Ahmad Alomari, Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering Expert Systems with Applications. ,vol. 84, pp. 24- 36 ,(2017) , 10.1016/J.ESWA.2017.05.002