Literatura académica sobre el tema "SUMMARIZATION ALGORITHMS"
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Artículos de revistas sobre el tema "SUMMARIZATION ALGORITHMS"
Chang, Hsien-Tsung, Shu-Wei Liu y Nilamadhab Mishra. "A tracking and summarization system for online Chinese news topics". Aslib Journal of Information Management 67, n.º 6 (16 de noviembre de 2015): 687–99. http://dx.doi.org/10.1108/ajim-10-2014-0147.
Texto completoYadav, Divakar, Naman Lalit, Riya Kaushik, Yogendra Singh, Mohit, Dinesh, Arun Kr Yadav, Kishor V. Bhadane, Adarsh Kumar y Baseem Khan. "Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain". Computational Intelligence and Neuroscience 2022 (9 de febrero de 2022): 1–14. http://dx.doi.org/10.1155/2022/3411881.
Texto completoMall, Shalu, Avinash Maurya, Ashutosh Pandey y Davain Khajuria. "Centroid Based Clustering Approach for Extractive Text Summarization". International Journal for Research in Applied Science and Engineering Technology 11, n.º 6 (30 de junio de 2023): 3404–9. http://dx.doi.org/10.22214/ijraset.2023.53542.
Texto completoBOKAEI, MOHAMMAD HADI, HOSSEIN SAMETI y YANG LIU. "Extractive summarization of multi-party meetings through discourse segmentation". Natural Language Engineering 22, n.º 1 (4 de marzo de 2015): 41–72. http://dx.doi.org/10.1017/s1351324914000199.
Texto completoDutta, Soumi, Vibhash Chandra, Kanav Mehra, Asit Kumar Das, Tanmoy Chakraborty y Saptarshi Ghosh. "Ensemble Algorithms for Microblog Summarization". IEEE Intelligent Systems 33, n.º 3 (mayo de 2018): 4–14. http://dx.doi.org/10.1109/mis.2018.033001411.
Texto completoHan, Kai, Shuang Cui, Tianshuai Zhu, Enpei Zhang, Benwei Wu, Zhizhuo Yin, Tong Xu, Shaojie Tang y He Huang. "Approximation Algorithms for Submodular Data Summarization with a Knapsack Constraint". ACM SIGMETRICS Performance Evaluation Review 49, n.º 1 (22 de junio de 2022): 65–66. http://dx.doi.org/10.1145/3543516.3453922.
Texto completoHan, Kai, Shuang Cui, Tianshuai Zhu, Enpei Zhang, Benwei Wu, Zhizhuo Yin, Tong Xu, Shaojie Tang y He Huang. "Approximation Algorithms for Submodular Data Summarization with a Knapsack Constraint". Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, n.º 1 (18 de febrero de 2021): 1–31. http://dx.doi.org/10.1145/3447383.
Texto completoPopescu, Claudiu, Lacrimioara Grama y Corneliu Rusu. "A Highly Scalable Method for Extractive Text Summarization Using Convex Optimization". Symmetry 13, n.º 10 (30 de septiembre de 2021): 1824. http://dx.doi.org/10.3390/sym13101824.
Texto completoBoussaid, L., A. Mtibaa, M. Abid y M. Paindavoin. "Real-Time Algorithms for Video Summarization". Journal of Applied Sciences 6, n.º 8 (1 de abril de 2006): 1679–85. http://dx.doi.org/10.3923/jas.2006.1679.1685.
Texto completoKe, Xiangyu, Arijit Khan y Francesco Bonchi. "Multi-relation Graph Summarization". ACM Transactions on Knowledge Discovery from Data 16, n.º 5 (31 de octubre de 2022): 1–30. http://dx.doi.org/10.1145/3494561.
Texto completoTesis sobre el tema "SUMMARIZATION ALGORITHMS"
Kolla, Maheedhar y University of Lethbridge Faculty of Arts and Science. "Automatic text summarization using lexical chains : algorithms and experiments". Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2004, 2004. http://hdl.handle.net/10133/226.
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Hodulik, George M. "Graph Summarization: Algorithms, Trained Heuristics, and Practical Storage Application". Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1482143946391013.
Texto completoHamid, Fahmida. "Evaluation Techniques and Graph-Based Algorithms for Automatic Summarization and Keyphrase Extraction". Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862796/.
Texto completoChiarandini, Luca. "Characterizing and modeling web sessions with applications". Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/283414.
Texto completoEsta tesis se centra en el análisis y modelaje de sesiones web, grupos de solicitudes realizadas por un único usuario para un sólo propósito de navegación. La comprensión de cómo la gente navega a través de los sitios web es importante para mejorar la interfaz y ofrecer un mejor contenido. En primer lugar, se realiza un análisis estadístico de las sesiones web. En segundo lugar, se presentan los algoritmos para identificar los patrones de navegación frecuentes y modelar las sesiones web. Finalmente, se describen varias aplicaciones que utilizan nuevas formas de navegación: la navegación paralela. A través del análisis de los registros de uso se observa que las personas tienden a navegar por las imágenes en modo secuencial y que esas secuencias pueden ser consideradas como unidades de contenido. % La generación de resumenes de sesiones presentada en esta tesis es un problema nuevo de extracción de patrones y se puede aplicar también a otros campos como el de la propagación de información. A partir del análisis y los modelos presentados entendemos que la información contextual, como el dominio previo de acceso o la hora del día, juega un papel importante en la evolución de las sesiones. Para entender la navegación no se debe, por tanto, olvidar el contexto en que esta se lleva a cabo.
Bahri, Maroua. "Improving IoT data stream analytics using summarization techniques". Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT017.
Texto completoWith the evolution of technology, the use of smart Internet-of-Things (IoT) devices, sensors, and social networks result in an overwhelming volume of IoT data streams, generated daily from several applications, that can be transformed into valuable information through machine learning tasks. In practice, multiple critical issues arise in order to extract useful knowledge from these evolving data streams, mainly that the stream needs to be efficiently handled and processed. In this context, this thesis aims to improve the performance (in terms of memory and time) of existing data mining algorithms on streams. We focus on the classification task in the streaming framework. The task is challenging on streams, principally due to the high -- and increasing -- data dimensionality, in addition to the potentially infinite amount of data. The two aspects make the classification task harder.The first part of the thesis surveys the current state-of-the-art of the classification and dimensionality reduction techniques as applied to the stream setting, by providing an updated view of the most recent works in this vibrant area.In the second part, we detail our contributions to the field of classification in streams, by developing novel approaches based on summarization techniques aiming to reduce the computational resource of existing classifiers with no -- or minor -- loss of classification accuracy. To address high-dimensional data streams and make classifiers efficient, we incorporate an internal preprocessing step that consists in reducing the dimensionality of input data incrementally before feeding them to the learning stage. We present several approaches applied to several classifications tasks: Naive Bayes which is enhanced with sketches and hashing trick, k-NN by using compressed sensing and UMAP, and also integrate them in ensemble methods
Santos, Joelson Antonio dos. "Algoritmos rápidos para estimativas de densidade hierárquicas e suas aplicações em mineração de dados". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25102018-174244/.
Texto completoClustering is an unsupervised learning task able to describe a set of objects in clusters, so that objects of a same cluster are more similar than objects of other clusters. Clustering techniques are divided in two main categories: partitional and hierarchical. The particional techniques divide a dataset into a number of distinct clusters, while hierarchical techniques provide a nested sequence of partitional clusters separated by different levels of granularity. Furthermore, hierarchical density-based clustering is a particular clustering paradigm that detects clusters with different concentrations or densities of objects. One of the most popular techniques of this paradigm is known as HDBSCAN*. In addition to providing hierarchies, HDBSCAN* is a framework that provides outliers detection, semi-supervised clustering and visualization of results. However, most hierarchical techniques, including HDBSCAN*, have a high complexity computational. This fact makes them prohibitive for the analysis of large datasets. In this work have been proposed two approximate variations of HDBSCAN* computationally more scalable for clustering large amounts of data. The first variation follows the concept of parallel and distributed computing, known as MapReduce. The second one follows the context of parallel computing using shared memory. Both variations are based on a concept of efficient data division, known as Recursive Sampling, which allows parallel processing of this data. In a manner similar to HDBSCAN*, the proposed variations are also capable of providing complete unsupervised patterns analysis in data, including outliers detection. Experiments have been carried out to evaluate the quality of the variations proposed in this work, specifically, the variation based on MapReduce have been compared to a parallel and exact version of HDBSCAN*, known as Random Blocks. Already the version parallel in shared memory environment have been compared to the state of the art (HDBSCAN*). In terms of clustering quality and outliers detection, the variation based on MapReduce and other based on shared memory showed results close to the exact parallel verson of HDBSCAN* and the state of the art, respectively. In terms of computational time, the proposed variations showed greater scalability and speed for processing large amounts of data than the compared versions.
Krübel, Monique. "Analyse und Vergleich von Extraktionsalgorithmen für die Automatische Textzusammenfassung". Master's thesis, Universitätsbibliothek Chemnitz, 2006. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200601180.
Texto completoMaaloul, Mohamed. "Approche hybride pour le résumé automatique de textes : Application à la langue arabe". Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4778.
Texto completoThis thesis falls within the framework of Natural Language Processing. The problems of automatic summarization of Arabic documents which was approached, in this thesis, are based on two points. The first point relates to the criteria used to determine the essential content to extract. The second point focuses on the means to express the essential content extracted in the form of a text targeting the user potential needs.In order to show the feasibility of our approach, we developed the "L.A.E" system, based on a hybrid approach which combines a symbolic analysis with a numerical processing.The evaluation results are encouraging and prove the performance of the proposed hybrid approach.These results showed, initially, the applicability of the approach in the context of mono documents without restriction as for their topics (Education, Sport, Science, Politics, Interaction, etc), their content and their volume. They also showed the importance of the machine learning in the phase of classification and selection of the sentences forming the final extract
Pokorný, Lubomír. "Metody sumarizace textových dokumentů". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236443.
Texto completoHassanlou, Nasrin. "Probabilistic graph summarization". Thesis, 2012. http://hdl.handle.net/1828/4403.
Texto completoGraduate
Capítulos de libros sobre el tema "SUMMARIZATION ALGORITHMS"
Tian, Yuanyuan y Jignesh M. Patel. "Interactive Graph Summarization". En Link Mining: Models, Algorithms, and Applications, 389–409. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6515-8_15.
Texto completoJaved, Hira, M. M. Sufyan Beg y Nadeem Akhtar. "Multimodal Summarization: A Concise Review". En Algorithms for Intelligent Systems, 613–23. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6893-7_54.
Texto completoKomorowski, Artur, Lucjan Janowski y Mikołaj Leszczuk. "Evaluation of Multimedia Content Summarization Algorithms". En Cryptology and Network Security, 424–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98678-4_43.
Texto completoZhao, Yu, Songping Huang, Dongsheng Zhou, Zhaoyun Ding, Fei Wang y Aixin Nian. "CNsum: Automatic Summarization for Chinese News Text". En Wireless Algorithms, Systems, and Applications, 539–47. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19214-2_45.
Texto completoSharma, Arjun Datt y Shaleen Deep. "Too Long-Didn’t Read: A Practical Web Based Approach towards Text Summarization". En Applied Algorithms, 198–208. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04126-1_17.
Texto completoGokul Amuthan, S. y S. Chitrakala. "CESumm: Semantic Graph-Based Approach for Extractive Text Summarization". En Algorithms for Intelligent Systems, 89–100. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3246-4_8.
Texto completoChen, Chen, Cindy Xide Lin, Matt Fredrikson, Mihai Christodorescu, Xifeng Yan y Jiawei Han. "Mining Large Information Networks by Graph Summarization". En Link Mining: Models, Algorithms, and Applications, 475–501. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6515-8_18.
Texto completoTsitovich, Aliaksei, Natasha Sharygina, Christoph M. Wintersteiger y Daniel Kroening. "Loop Summarization and Termination Analysis". En Tools and Algorithms for the Construction and Analysis of Systems, 81–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19835-9_9.
Texto completoRehman, Tohida, Suchandan Das, Debarshi Kumar Sanyal y Samiran Chattopadhyay. "An Analysis of Abstractive Text Summarization Using Pre-trained Models". En Algorithms for Intelligent Systems, 253–64. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1657-1_21.
Texto completoNadaf, Shatajbegum y Vidyagouri B. Hemadri. "Extractive Summarization of Text Using Weighted Average of Feature Scores". En Algorithms for Intelligent Systems, 223–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4893-6_20.
Texto completoActas de conferencias sobre el tema "SUMMARIZATION ALGORITHMS"
Aldeghlawi, Maher, Mohammed Q. Alkhatib y Miguel Velez-Reyes. "Data summarization for hyperspectral image analysis". En Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, editado por David W. Messinger y Miguel Velez-Reyes. SPIE, 2021. http://dx.doi.org/10.1117/12.2590762.
Texto completoTatar, Doina, Andreea Diana Mihis y Gabriela Serban Czibula. "Lexical Chains Segmentation in Summarization". En 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE, 2008. http://dx.doi.org/10.1109/synasc.2008.11.
Texto completoThakkar, K. S., R. V. Dharaskar y M. B. Chandak. "Graph-Based Algorithms for Text Summarization". En Third International Conference on Emerging Trends in Engineering and Technology (ICETET 2010). IEEE, 2010. http://dx.doi.org/10.1109/icetet.2010.104.
Texto completoBoonchaisuk, Prayote y Kanda Runapongsa Saikaew. "Efficient algorithms for Thai tweet summarization". En 2016 International Computer Science and Engineering Conference (ICSEC). IEEE, 2016. http://dx.doi.org/10.1109/icsec.2016.7859926.
Texto completoLiu, Jie, Fuzhen Chen, Xianguo Ma, Zuoyan Gong, Jianliang Zhang, Zhengjian Liu, Yaozu Wang y YunFei Ma. "Summarization of Sinter Quality Prediction Algorithms". En 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 2022. http://dx.doi.org/10.1109/yac57282.2022.10023825.
Texto completoDutulescu, Andreea Nicoleta, Mihai Dascalu y Stefan Ruseti. "Unsupervised Extractive Summarization with BERT". En 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2022. http://dx.doi.org/10.1109/synasc57785.2022.00032.
Texto completoLiu, Na, Xiao-Jun Tang, Ying Lu, Ming-Xia Li, Hai-Wen Wang y Peng Xiao. "Topic-Sensitive Multi-document Summarization Algorithm". En 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE, 2014. http://dx.doi.org/10.1109/paap.2014.22.
Texto completoLi, Cong, Shuangxiong Wei, Yuxuan Liu, Siyi Luo, Di Yang y Zengkai Wang. "Attention based fully convolutional network for video summarization". En International Conference on Algorithms, Microchips, and Network Applications, editado por Fengjie Cen y Ning Sun. SPIE, 2022. http://dx.doi.org/10.1117/12.2636379.
Texto completoOlariu, Andrei. "Clustering to Improve Microblog Stream Summarization". En 2012 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2012. http://dx.doi.org/10.1109/synasc.2012.10.
Texto completoDey, Tamal K., Facundo Mémoli y Yusu Wang. "Multiscale Mapper: Topological Summarization via Codomain Covers". En Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974331.ch71.
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