Relevant bibliographies by topics / Text Summarization, Latent Semantic Analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Text Summarization, Latent Semantic Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Text Summarization, Latent Semantic Analysis"

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Ozsoy, Makbule Gulcin, Ferda Nur Alpaslan, and Ilyas Cicekli. "Text summarization using Latent Semantic Analysis." Journal of Information Science 37, no. 4 (2011): 405–17. http://dx.doi.org/10.1177/0165551511408848.

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Ba-Alwi, Fadl, Ghaleb Gaphari, and Fares Al-Duqaimi. "Arabic Text Summarization Using Latent Semantic Analysis." British Journal of Applied Science & Technology 10, no. 2 (2015): 1–14. http://dx.doi.org/10.9734/bjast/2015/17678.

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Mashechkin, I. V., M. I. Petrovskiy, D. S. Popov, and D. V. Tsarev. "Automatic text summarization using latent semantic analysis." Programming and Computer Software 37, no. 6 (2011): 299–305. http://dx.doi.org/10.1134/s0361768811060041.

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Omar, Abdulfattah. "Addressing the Problem of Coherence in Automatic Text Summarization: A Latent Semantic Analysis Approach." International Journal of English Linguistics 7, no. 4 (2017): 33. http://dx.doi.org/10.5539/ijel.v7n4p33.

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This article is concerned with addressing the problem of coherence in the automatic summarization of prose fiction texts. Despite the increasing advances within the summarization theory, applications and industry, many problems are still unresolved in relations to the applications of the summarization theory to literature. This can be in part attributed to the peculiar nature of literary texts where standard or typical summarization processes are not amenable for literature. This study, therefore, tends to bridge the gap between literature and summarization theory by proposing a summarization system that is based on more semantic-based approaches for extracting more meaningful and coherent summaries. Given that lack of coherence within summaries has its negative implications on understanding original texts; it follows that more effective methods should be developed in relation to the extraction of coherent summaries. In order to do this, a hybrid of methods including statistical (TF-IDF) and semantic (Latent Semantic Analysis LSA) methods were used to derive the most distinctive features and extract summaries from 10 English novellas. For evaluation purposes, both intrinsic and extrinsic methods are used for determining the quality of the extracted summaries. Results indicate that the integration of LSA into features extraction methods achieves better summarization performance outcomes in terms of coherence properties within the extracted summaries.
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MohammedBadry, Rasha, Ahmed Sharaf Eldin, and Doaa Saad Elzanfally. "Text Summarization within the Latent Semantic Analysis Framework: Comparative Study." International Journal of Computer Applications 81, no. 11 (2013): 40–45. http://dx.doi.org/10.5120/14060-2366.

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Yeh, Jen-Yuan, Hao-Ren Ke, Wei-Pang Yang, and I.-Heng Meng. "Text summarization using a trainable summarizer and latent semantic analysis." Information Processing & Management 41, no. 1 (2005): 75–95. http://dx.doi.org/10.1016/j.ipm.2004.04.003.

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Joshi, Manju Lata, Nisheeth Joshi, and Namita Mittal. "SGATS: Semantic Graph-based Automatic Text Summarization from Hindi Text Documents." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (2021): 1–32. http://dx.doi.org/10.1145/3464381.

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Creating a coherent summary of the text is a challenging task in the field of Natural Language Processing (NLP). Various Automatic Text Summarization techniques have been developed for abstractive as well as extractive summarization. This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning, Latent Semantic Analysis (LSA), neural networks, cluster, and fuzzy logic. In this paper, a semantic graph-based approach SGATS (Semantic Graph-based approach for Automatic Text Summarization) is proposed to generate an extractive summary. The proposed approach constructs a semantic graph of the original Hindi text document by establishing a semantic relationship between sentences of the document using Hindi Wordnet ontology as a background knowledge source. Once the semantic graph is constructed, fourteen different graph theoretical measures are applied to rank the document sentences depending on their semantic scores. The proposed approach is applied to two data sets of different domains of Tourism and Health. The performance of the proposed approach is compared with the state-of-the-art TextRank algorithm and human-annotated summary. The performance of the proposed system is evaluated using widely accepted ROUGE measures. The outcomes exhibit that our proposed system produces better results than TextRank for health domain corpus and comparable results for tourism corpus. Further, correlation coefficient methods are applied to find a correlation between eight different graphical measures and it is observed that most of the graphical measures are highly correlated.
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N, Mehala, and Tapas Guha. "Latent Semantic Analysis in Automatic Text Summarization: A state of the art analysis." International Journal of Intelligence and Sustainable Computing 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijisc.2020.10029282.

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Froud, Hanane, Abdelmonaime Lachkar, and Said Alaoui Ouatik. "Arabic Text Summarization Based on Latent Semantic Analysis to Enhance Arabic Documents Clustering." International Journal of Data Mining & Knowledge Management Process 3, no. 1 (2013): 79–95. http://dx.doi.org/10.5121/ijdkp.2013.3107.

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Liang, Chao, Changsheng Xu, and Hanqing Lu. "Personalized Sports Video Customization Using Content and Context Analysis." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/836357.

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We present an integrated framework on personalized sports video customization, which addresses three research issues: semantic video annotation, personalized video retrieval and summarization, and system adaptation. Sports video annotation serves as the foundation of the video customization system. To acquire detailed description of video content, external web text is adopted to align with the related sports video according to their semantic correspondence. Based on the derived semantic annotation, a user-participant multiconstraint 0/1 Knapsack model is designed to model the personalized video customization, which can unify both video retrieval and summarization with different fusion parameters. As a measure to make the system adaptive to the particular user, a social network based system adaptation algorithm is proposed to learn latent user preference implicitly. Both quantitative and qualitative experiments conducted on twelve broadcast basketball and football videos validate the effectiveness of the proposed method.
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Dissertations / Theses on the topic "Text Summarization, Latent Semantic Analysis"

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Ozsoy, Makbule Gulcin. "Text Summarization Using Latent Semantic Analysis." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612988/index.pdf.

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Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to create well formed summaries in literature. One of the newest methods in text summarization is the Latent Semantic Analysis (LSA) method. In this thesis, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores.
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Geiss, Johanna. "Latent semantic sentence clustering for multi-document summarization." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609761.

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Huang, Fang. "Multi-document summarization with latent semantic analysis." Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419255.

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Belica, Michal. "Metody sumarizace dokumentů na webu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236386.

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The work deals with automatic summarization of documents in HTML format. As a language of web documents, Czech language has been chosen. The project is focused on algorithms of text summarization. The work also includes document preprocessing for summarization and conversion of text into representation suitable for summarization algorithms. General text mining is also briefly discussed but the project is mainly focused on the automatic document summarization. Two simple summarization algorithms are introduced. Then, the main attention is paid to an advanced algorithm that uses latent semantic analysis. Result of the work is a design and implementation of summarization module for Python language. Final part of the work contains evaluation of summaries generated by implemented summarization methods and their subjective comparison of the author.
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Sheikha, Hassan. "Text mining Twitter social media for Covid-19 : Comparing latent semantic analysis and latent Dirichlet allocation." Thesis, Högskolan i Gävle, Avdelningen för datavetenskap och samhällsbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-32567.

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In this thesis, the Twitter social media is data mined for information about the covid-19 outbreak during the month of March, starting from the 3’rd and ending on the 31’st. 100,000 tweets were collected from Harvard’s opensource data and recreated using Hydrate. This data is analyzed further using different Natural Language Processing (NLP) methodologies, such as termfrequency inverse document frequency (TF-IDF), lemmatizing, tokenizing, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Furthermore, the results of the LSA and LDA algorithms is reduced dimensional data that will be clustered using clustering algorithms HDBSCAN and K-Means for later comparison. Different methodologies are used to determine the optimal parameters for the algorithms. This is all done in the python programing language, as there are libraries for supporting this research, the most important being scikit-learn. The frequent words of each cluster will then be displayed and compared with factual data regarding the outbreak to discover if there are any correlations. The factual data is collected by World Health Organization (WHO) and is then visualized in graphs in ourworldindata.org. Correlations with the results are also looked for in news articles to find any significant moments to see if that affected the top words in the clustered data. The news articles with good timelines used for correlating incidents are that of NBC News and New York Times. The results show no direct correlations with the data reported by WHO, however looking into the timelines reported by news sources some correlation can be seen with the clustered data. Also, the combination of LDA and HDBSCAN yielded the most desireable results in comparison to the other combinations of the dimnension reductions and clustering. This was much due to the use of GridSearchCV on LDA to determine the ideal parameters for the LDA models on each dataset as well as how well HDBSCAN clusters its data in comparison to K-Means.
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Ashton, Triss A. "Accuracy and Interpretability Testing of Text Mining Methods." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc283791/.

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Extracting meaningful information from large collections of text data is problematic because of the sheer size of the database. However, automated analytic methods capable of processing such data have emerged. These methods, collectively called text mining first began to appear in 1988. A number of additional text mining methods quickly developed in independent research silos with each based on unique mathematical algorithms. How good each of these methods are at analyzing text is unclear. Method development typically evolves from some research silo centric requirement with the success of the method measured by a custom requirement-based metric. Results of the new method are then compared to another method that was similarly developed. The proposed research introduces an experimentally designed testing method to text mining that eliminates research silo bias and simultaneously evaluates methods from all of the major context-region text mining method families. The proposed research method follows a random block factorial design with two treatments consisting of three and five levels (RBF-35) with repeated measures. Contribution of the research is threefold. First, the users perceived a difference in the effectiveness of the various methods. Second, while still not clear, there are characteristics with in the text collection that affect the algorithms ability to extract meaningful results. Third, this research develops an experimental design process for testing the algorithms that is adaptable into other areas of software development and algorithm testing. This design eliminates the bias based practices historically employed by algorithm developers.
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Papadouka, Maria Eirini. "Using Topic Models to Study Journalist-Audience Convergence and Divergence: The Case of Human Trafficking Coverage on British Online Newspapers." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862882/.

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Despite the accessibility of online news and availability of sophisticated methods for analyzing news content, no previous study has focused on the simultaneous examination of news coverage on human trafficking and audiences' interpretations of this coverage. In my research, I have examined both journalists' and commenters' topic choices in coverage and discussion of human trafficking from the online platforms of three British newspapers covering the period 2009–2015. I used latent semantic analysis (LSA) to identify emergent topics in my corpus of newspaper articles and readers' comments, and I then quantitatively investigated topic preferences to identify convergence and divergence on the topics discussed by journalists and their readers. I addressed my research questions in two distinctive studies. The first case study implemented topic modelling techniques and further quantitative analyses on article and comment paragraphs from The Guardian. The second extensive study included article and comment paragraphs from the online platforms of three British newspapers: The Guardian, The Times and the Daily Mail. The findings indicate that the theories of "agenda setting" and of "active audience" are not mutually exclusive, and the scope of explanation of each depends partly on the specific topic or subtopic that is analyzed. Taking into account further theoretical concepts related to agenda setting, four more additional research questions were addressed. Topic convergence and divergence was further identified when taking into account the newspapers' political orientation and the articles' and comments' year of publication.
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Zougris, Konstantinos. "Sociological Applications of Topic Extraction Techniques: Two Case Studies." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804982/.

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Limited research has been conducted with regards to the applicability of topic extraction techniques in Sociology. Addressing the modern methodological opportunities, and responding to the skepticism with regards to the absence of theoretical foundations supporting the use of text analytics, I argue that Latent Semantic Analysis (LSA), complemented by other text analysis techniques and multivariate techniques, can constitute a unique hybrid method that can facilitate the sociological interpretations of web-based textual data. To illustrate the applicability of the hybrid technique, I developed two case studies. My first case study is associated with the Sociology of media. It focuses on the topic extraction and sentiment polarization among partisan texts posted on two major news sites. I find evidence of highly polarized opinions on comments posted on the Huffington Post and the Daily Caller. The highest polarizing topic was associated with a commentator’s reference on Hoodies in the context of the Trayvon Martin’s incident. My findings support contemporary research suggesting that media pundits frequently use tactics of outrage to provoke polarization of public opinion. My second case study contributes to the research domain of the Sociology of knowledge. The hybrid method revealed evidence of topical divides and topical “bridges” in the intellectual landscape of the British and the American sociological journals. My findings confirm the theoretical assertions describing Sociology as a fractured field, and partially support the existence of more globalized topics in the discipline.
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Pinheiro, José Claudio dos Santos. "USO DE TEORIAS NO CAMPO DE SISTEMAS DE INFORMAÇÃO: MAPEAMENTO USANDO TÉCNICAS DE MINERAÇÃO DE TEXTOS." Universidade Metodista de São Paulo, 2009. http://tede.metodista.br/jspui/handle/tede/152.

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Made available in DSpace on 2016-08-02T21:42:57Z (GMT). No. of bitstreams: 1 Jose Claudio dos Santos Pinheiro.pdf: 5349646 bytes, checksum: 057189cedae5b7fc79c3e7cec83d51aa (MD5) Previous issue date: 2009-09-17<br>This work aim to map the use of information system s theories, based on analytic resources that came from information retrieval techniques and data mining and text mining methodologies. The theories addressed by this research were Transactions Costs Economics (TCE), Resource-based view (RBV) and Institutional Theory (IT), which were chosen given their usefulness, while alternatives of approach in processes of allocation of investments and implementation of information systems. The empirical data are based on the content of textual data in abstract and review sections, of articles from ISR, MISQ and JIMS along the period from 2000 to 2008. The results stemming from the text mining technique combined with data mining were compared with the advanced search tool EBSCO and demonstrated greater efficiency in the identification of content. Articles based on three theories accounted for 10% of all articles of the three journals and the most useful publication was the 2001 and 2007.(AU)<br>Esta dissertação visa apresentar o mapeamento do uso das teorias de sistemas de informações, usando técnicas de recuperação de informação e metodologias de mineração de dados e textos. As teorias abordadas foram Economia de Custos de Transações (Transactions Costs Economics TCE), Visão Baseada em Recursos da Firma (Resource-Based View-RBV) e Teoria Institucional (Institutional Theory-IT), sendo escolhidas por serem teorias de grande relevância para estudos de alocação de investimentos e implementação em sistemas de informação, tendo como base de dados o conteúdo textual (em inglês) do resumo e da revisão teórica dos artigos dos periódicos Information System Research (ISR), Management Information Systems Quarterly (MISQ) e Journal of Management Information Systems (JMIS) no período de 2000 a 2008. Os resultados advindos da técnica de mineração textual aliada à mineração de dados foram comparadas com a ferramenta de busca avançada EBSCO e demonstraram uma eficiência maior na identificação de conteúdo. Os artigos fundamentados nas três teorias representaram 10% do total de artigos dos três períodicos e o período mais profícuo de publicação foi o de 2001 e 2007.(AU)
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Chen, Xi. "Learning with Sparcity: Structures, Optimization and Applications." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/228.

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The development of modern information technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, and data from scientific domains (e.g., meteorology). To learn from these high dimensional and complex data, traditional machine learning techniques often suffer from the curse of dimensionality and unaffordable computational cost. However, learning from large-scale high-dimensional data promises big payoffs in text mining, gene analysis, and numerous other consequential tasks. Recently developed sparse learning techniques provide us a suite of tools for understanding and exploring high dimensional data from many areas in science and engineering. By exploring sparsity, we can always learn a parsimonious and compact model which is more interpretable and computationally tractable at application time. When it is known that the underlying model is indeed sparse, sparse learning methods can provide us a more consistent model and much improved prediction performance. However, the existing methods are still insufficient for modeling complex or dynamic structures of the data, such as those evidenced in pathways of genomic data, gene regulatory network, and synonyms in text data. This thesis develops structured sparse learning methods along with scalable optimization algorithms to explore and predict high dimensional data with complex structures. In particular, we address three aspects of structured sparse learning: 1. Efficient and scalable optimization methods with fast convergence guarantees for a wide spectrum of high-dimensional learning tasks, including single or multi-task structured regression, canonical correlation analysis as well as online sparse learning. 2. Learning dynamic structures of different types of undirected graphical models, e.g., conditional Gaussian or conditional forest graphical models. 3. Demonstrating the usefulness of the proposed methods in various applications, e.g., computational genomics and spatial-temporal climatological data. In addition, we also design specialized sparse learning methods for text mining applications, including ranking and latent semantic analysis. In the last part of the thesis, we also present the future direction of the high-dimensional structured sparse learning from both computational and statistical aspects.
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Book chapters on the topic "Text Summarization, Latent Semantic Analysis"

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Steinberger, Josef, and Karel Ježek. "Update Summarization Based on Latent Semantic Analysis." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04208-9_14.

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Wang, Yingjie, and Jun Ma. "A Comprehensive Method for Text Summarization Based on Latent Semantic Analysis." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41644-6_38.

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Yeh, Jen-Yuan, Hao-Ren Ke, and Wei-Pang Yang. "Chinese Text Summarization Using a Trainable Summarizer and Latent Semantic Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36227-4_8.

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Chatterjee, Niladri, and Nidhika Yadav. "Hybrid Latent Semantic Analysis and Random Indexing Model for Text Summarization." In Information and Communication Technology for Competitive Strategies. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0586-3_15.

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Shah, Chintan, and Anjali Jivani. "An Automatic Text Summarization on Naive Bayes Classifier Using Latent Semantic Analysis." In Data, Engineering and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6347-4_16.

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Yang, Rui, Zhan Bu, and Zhengyou Xia. "Automatic Summarization for Chinese Text Using Affinity Propagation Clustering and Latent Semantic Analysis." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_67.

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Anandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Semantic Space Representation and Latent Semantic Analysis." In Practical Text Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_6.

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Anandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Latent Semantic Analysis (LSA) in Python." In Practical Text Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_14.

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Alami, Nabil, Yassine El Adlouni, Noureddine En-nahnahi, and Mohammed Meknassi. "Using Statistical and Semantic Analysis for Arabic Text Summarization." In International Conference on Information Technology and Communication Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64719-7_4.

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Hu, Jiani, Weihong Deng, and Jun Guo. "Robust Discriminant Analysis of Latent Semantic Feature for Text Categorization." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_46.

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Conference papers on the topic "Text Summarization, Latent Semantic Analysis"

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Geetha J K and Deepamala N. "Kannada text summarization using Latent Semantic Analysis." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275826.

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Merchant, Kaiz, and Yash Pande. "NLP Based Latent Semantic Analysis for Legal Text Summarization." In 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018. http://dx.doi.org/10.1109/icacci.2018.8554831.

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Gong, Yihong, and Xin Liu. "Generic text summarization using relevance measure and latent semantic analysis." In the 24th annual international ACM SIGIR conference. ACM Press, 2001. http://dx.doi.org/10.1145/383952.383955.

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Yirdaw, Eyob Delele, and Dejene Ejigu. "Topic-based Amharic text summarization with probabilistic latent semantic analysis." In the International Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2457276.2457279.

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Foong, Oi-Mean, Suet-Peng Yong, and Farha-Am Jaid. "Text Summarization Using Latent Semantic Analysis Model in Mobile Android Platform." In 2015 9th Asia Modelling Symposium (AMS). IEEE, 2015. http://dx.doi.org/10.1109/ams.2015.15.

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Chowdhury, Sohini Roy, Kamal Sarkar, and Santanu Dam. "An Approach to Generic Bengali Text Summarization Using Latent Semantic Analysis." In 2017 8th International Conference on Information Technology (ICIT). IEEE, 2017. http://dx.doi.org/10.1109/icit.2017.12.

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Shah, Chintan, and Anjali Jivani. "A Hybrid Approach of Text Summarization Using Latent Semantic Analysis and Deep Learning." In 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018. http://dx.doi.org/10.1109/icacci.2018.8554848.

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Yu, Jiangsheng, and Xue-Wen Chen. "Latent Topic-Semantic Indexing Based Automatic Text Summarization." In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2016. http://dx.doi.org/10.1109/icmla.2016.0028.

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Kherwa, Pooja, and Poonam Bansal. "Latent Semantic Analysis: An Approach to Understand Semantic of Text." In 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC). IEEE, 2017. http://dx.doi.org/10.1109/ctceec.2017.8455018.

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Jin, Xinyu, Wentao Ma, and Yunze Li. "Medical Record Text Analysis Based on Latent Semantic Analysis." In 2015 8th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2015. http://dx.doi.org/10.1109/iscid.2015.155.

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