![]() doi: 10.1016/j.eswa.2012.09.014Īlguliev RM, Aliguliyev RM, Hajirahimova MS, Mehdiyev CA (2011) MCMR: maximum coverage and minimum redundant text summarization model. Expert Syst Appl 36(4):7764–7772Īlguliev RM, Aliguliyev RM, Isazade NR (2013) Multiple documents summarization based on evolutionary optimization algorithm. In: International conference on information retrieval knowledge management, pp 193–197Īliguliyev RM (2009) A new sentence similarity measure and sentence based extractive technique for automatic text summarization. Finally this paper concludes with the discussion of useful future directions that can help researchers to identify areas where further research is needed.Ībuobieda A, Salim N, Albaham AT, Osman AH, Kumar YJ (2012) Text summarization features selection method using pseudo genetic-based model. Furthermore, evaluation results of extractive summarization approaches are presented on some shared DUC datasets. Therefore, intrinsic as well as extrinsic both the methods of summary evaluation are described in detail along with text summarization evaluation conferences and workshops. Summary evaluation is another challenging issue in this research field. A few abstractive and multilingual text summarization approaches are also covered. Their needs are identified and their advantages and disadvantages are listed in a comparative manner. This paper presents a comprehensive survey of recent text summarization extractive approaches developed in the last decade. During a decade, several extractive approaches have been developed for automatic summary generation that implements a number of machine learning and optimization techniques. Therefore, research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. Abstractive methods are highly complex as they need extensive natural language processing. Summary can be generated through extractive as well as abstractive methods. Since the advent of text summarization in 1950s, researchers have been trying to improve techniques for generating summaries so that machine generated summary matches with the human made summary. ![]() short length text that includes all the important information of the document. Automatic text summarization system generates a summary, i.e. Hence, there is growing interest among the research community for developing new approaches to automatically summarize the text. As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users.
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