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ISSN Druckformat: 1064-2315
ISSN Online: 2163-9337
Indexed in
Automatic Text Documents Summarization through Sentences Clustering
ABSTRAKT
To provide minimum redundancy in summary and maximum possible covering of the document content one has proposed the method of automatic summarization of the text documents based on sentence clustering. Sentence clustering is applied to determining topics and informative sentences. The clustering problem is formulated as a binary quadratic interger programming problem. The number of clusters (topics) is determined by deliberately developed algorithm. The synthesis algorithm of neural net for solving clustering problem has been described.
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Alguliev Rasim M., Aliguliyev Ramiz M., Hajirahimova Makrufa S., Mehdiyev Chingiz A., MCMR: Maximum coverage and minimum redundant text summarization model, Expert Systems with Applications, 38, 12, 2011. Crossref
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Alguliev Rasim M., Aliguliyev Ramiz M., Isazade Nijat R., Formulation of document summarization as a 0–1 nonlinear programming problem, Computers & Industrial Engineering, 64, 1, 2013. Crossref
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Alguliev Rasim M., Aliguliyev Ramiz M., Hajirahimova Makrufa S., GenDocSum+MCLR: Generic document summarization based on maximum coverage and less redundancy, Expert Systems with Applications, 39, 16, 2012. Crossref
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Alguliev Rasim M., Aliguliyev Ramiz M., Mehdiyev Chingiz A., AN OPTIMIZATION APPROACH TO AUTOMATIC GENERIC DOCUMENT SUMMARIZATION, Computational Intelligence, 29, 1, 2013. Crossref
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Alguliev Rasim M., Aliguliyev Ramiz M., Isazade Nijat R., CDDS: Constraint-driven document summarization models, Expert Systems with Applications, 40, 2, 2013. Crossref
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Alguliev Rasim M., Aliguliyev Ramiz M., Isazade Nijat R., Multiple documents summarization based on evolutionary optimization algorithm, Expert Systems with Applications, 40, 5, 2013. Crossref
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ALGULIEV RASIM M., ALIGULIYEV RAMIZ M., ISAZADE NIJAT R., MR&MR-SUM: MAXIMUM RELEVANCE AND MINIMUM REDUNDANCY DOCUMENT SUMMARIZATION MODEL, International Journal of Information Technology & Decision Making, 12, 03, 2013. Crossref
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Alguliev Rasim M., Aliguliyev Ramiz M., Nazirova Saadat A., Classification of Textual E-Mail Spam Using Data Mining Techniques, Applied Computational Intelligence and Soft Computing, 2011, 2011. Crossref
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Karwa Shweta, Chatterjee Niladri, Discrete Differential Evolution for Text Summarization, 2014 International Conference on Information Technology, 2014. Crossref
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Alguliev Rasim, Aliguliyev Ramiz, Hajirahimova Makrufa, Multi-Document Summarization Model Based on Integer Linear Programming, Intelligent Control and Automation, 01, 02, 2010. Crossref
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Qumsiyeh Rani, Ng Yiu-Kai, Enhancing web search by using query-based clusters and multi-document summaries, Knowledge and Information Systems, 47, 2, 2016. Crossref
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Alguliyev Rasim M., Aliguliyev Ramiz M., Isazade Nijat R., An unsupervised approach to generating generic summaries of documents, Applied Soft Computing, 34, 2015. Crossref
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Qumsiyeh Rani, Ng Yiu-Kai, Web Search Using Summarization on Clustered Web Documents Retrieved by User Queries, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2015. Crossref
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ALGULIEV Rasim, ALIGULIYEV Ramiz, Evolutionary Algorithm for Extractive Text Summarization, Intelligent Information Management, 01, 02, 2009. Crossref
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Alguliyev Rasim M., Aliguliyev Ramiz M., Isazade Nijat R., Abdi Asad, Idris Norisma, COSUM: Text summarization based on clustering and optimization, Expert Systems, 36, 1, 2019. Crossref
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Joshi Akanksha, Fidalgo E., Alegre E., Fernández-Robles Laura, SummCoder: An unsupervised framework for extractive text summarization based on deep auto-encoders, Expert Systems with Applications, 129, 2019. Crossref
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Thornton Megan, Gao Sophie, Ng Yiu-Kai, A Simple, Concise, Query-based Approach to News Article Summarization Using Sentence Scoring, 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), 2021. Crossref