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Artykuły w czasopismach na temat "Summarization"
da Cunha, Iria, Leo Wanner i Teresa Cabré. "Summarization of specialized discourse". Terminology 13, nr 2 (19.11.2007): 249–86. http://dx.doi.org/10.1075/term.13.2.07cun.
Pełny tekst źródłaSirohi, Neeraj Kumar, Dr Mamta Bansal i Dr S. N. Rajan Rajan. "Text Summarization Approaches Using Machine Learning & LSTM". Revista Gestão Inovação e Tecnologias 11, nr 4 (1.09.2021): 5010–26. http://dx.doi.org/10.47059/revistageintec.v11i4.2526.
Pełny tekst źródłaBlekanov, Ivan S., Nikita Tarasov i Svetlana S. Bodrunova. "Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages". Future Internet 14, nr 3 (23.02.2022): 69. http://dx.doi.org/10.3390/fi14030069.
Pełny tekst źródłaPei, Jisheng, i Xiaojun Ye. "Information-Balance-Aware Approximated Summarization of Data Provenance". Scientific Programming 2017 (12.09.2017): 1–11. http://dx.doi.org/10.1155/2017/4504589.
Pełny tekst źródłaBhatia, Neelima, i Arunima Jaiswal. "Literature Review on Automatic Text Summarization: Single and Multiple Summarizations". International Journal of Computer Applications 117, nr 6 (20.05.2015): 25–29. http://dx.doi.org/10.5120/20560-2948.
Pełny tekst źródłaZhang, Qianjin, Dahai Jin, Yawen Wang i Yunzhan Gong. "Statement-Grained Hierarchy Enhanced Code Summarization". Electronics 13, nr 4 (15.02.2024): 765. http://dx.doi.org/10.3390/electronics13040765.
Pełny tekst źródłaS, Sai Shashank, Sindhu S, Vineeth V i Pranathi C. "VIDEO SUMMARIZATION". International Research Journal of Computer Science 9, nr 8 (13.08.2022): 277–80. http://dx.doi.org/10.26562/irjcs.2022.v0908.24.
Pełny tekst źródłaNenkova, Ani. "Automatic Summarization". Foundations and Trends® in Information Retrieval 5, nr 2 (2011): 103–233. http://dx.doi.org/10.1561/1500000015.
Pełny tekst źródłaLarson, Martha. "Automatic Summarization". Foundations and Trends® in Information Retrieval 5, nr 3 (2012): 235–422. http://dx.doi.org/10.1561/1500000020.
Pełny tekst źródłaD, Manju, Radhamani V, Dhanush Kannan A, Kavya B, Sangavi S i Swetha Srinivasan. "TEXT SUMMARIZATION". YMER Digital 21, nr 07 (7.07.2022): 173–82. http://dx.doi.org/10.37896/ymer21.07/13.
Pełny tekst źródłaRozprawy doktorskie na temat "Summarization"
Bosma, Wauter Eduard. "Discourse oriented summarization". Enschede : Centre for Telematics and Information Technology (CTIT), 2008. http://doc.utwente.nl/58836.
Pełny tekst źródłaMoon, Brandon B. "Interactive football summarization /". Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3337.pdf.
Pełny tekst źródłaMoon, Brandon B. "Interactive Football Summarization". BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1999.
Pełny tekst źródłaSizov, Gleb. "Extraction-Based Automatic Summarization : Theoretical and Empirical Investigation of Summarization Techniques". Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10861.
Pełny tekst źródłaA summary is a shortened version of a text that contains the main points of the original content. Automatic summarization is the task of generating a summary by a computer. For example, given a collection of news articles for the last week an automatic summarizer is able to create a concise overview of the important events. This summary can be used as the replacement for the original content or help to identify the events that a person is particularly interested in. Potentially, automatic summarization can save a lot of time for people that deal with a large amount of textual information. The straightforward way to generate a summary is to select several sentences from the original text and organize them in way to create a coherent text. This approach is called extraction-based summarization and is the topic of this thesis. Extraction-based summarization is a complex task that consists of several challenging subtasks. The essential part of the extraction-based approach is identification of sentences that contain important information. It can be done using graph-based representations and centrality measures that exploit similarities between sentences to identify the most central sentences. This thesis provide a comprehensive overview of methods used in extraction-based automatic summarization. In addition, several general natural language processing issues such as feature selection and text representation models are discussed with regard to automatic summarization. Part of the thesis is dedicated to graph-based representations and centrality measures used in extraction-based summarization. Theoretical analysis is reinforced with the experiments using the summarization framework implemented for this thesis. The task for the experiments is query-focused multi-document extraction-based summarization, that is, summarization of several documents according to a user query. The experiments investigate several approaches to this task as well as the use of different representation models, similarity and centrality measures. The obtained results indicate that use of graph centrality measures significantly improves the quality of generated summaries. Among the variety of centrality measure the degree-based ones perform better than path-based measures. The best performance is achieved when centralities are combined with redundancy removal techniques that prevent inclusion of similar sentences in a summary. Experiments with representation models reveal that a simple local term count representation performs better than the distributed representation based on latent semantic analysis, which indicates that further investigation of distributed representations in regard to automatic summarization is necessary. The implemented system performs quite good compared with the systems that participated in DUC 2007 summarization competition. Nevertheless, manual inspection of the generated summaries demonstrate some of the flaws of the implemented summarization mechanism that can be addressed by introducing advanced algorithms for sentence simplification and sentence ordering.
Chellal, Abdelhamid. "Event summarization on social media stream : retrospective and prospective tweet summarization". Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30118/document.
Pełny tekst źródłaUser-generated content on social media, such as Twitter, provides in many cases, the latest news before traditional media, which allows having a retrospective summary of events and being updated in a timely fashion whenever a new development occurs. However, social media, while being a valuable source of information, can be also overwhelming given the volume and the velocity of published information. To shield users from being overwhelmed by irrelevant and redundant posts, retrospective summarization and prospective notification (real-time summarization) were introduced as two complementary tasks of information seeking on document streams. The former aims to select a list of relevant and non-redundant tweets that capture "what happened". In the latter, systems monitor the live posts stream and push relevant and novel notifications as soon as possible. Our work falls within these frameworks and focuses on developing a tweet summarization approaches for the two aforementioned scenarios. It aims at providing summaries that capture the key aspects of the event of interest to help users to efficiently acquire information and follow the development of long ongoing events from social media. Nevertheless, tweet summarization task faces many challenges that stem from, on one hand, the high volume, the velocity and the variety of the published information and, on the other hand, the quality of tweets, which can vary significantly. In the prospective notification, the core task is the relevancy and the novelty detection in real-time. For timeliness, a system may choose to push new updates in real-time or may choose to trade timeliness for higher notification quality. Our contributions address these levels: First, we introduce Word Similarity Extended Boolean Model (WSEBM), a relevance model that does not rely on stream statistics and takes advantage of word embedding model. We used word similarity instead of the traditional weighting techniques. By doing this, we overcome the shortness and word mismatch issues in tweets. The intuition behind our proposition is that context-aware similarity measure in word2vec is able to consider different words with the same semantic meaning and hence allows offsetting the word mismatch issue when calculating the similarity between a tweet and a topic. Second, we propose to compute the novelty score of the incoming tweet regarding all words of tweets already pushed to the user instead of using the pairwise comparison. The proposed novelty detection method scales better and reduces the execution time, which fits real-time tweet filtering. Third, we propose an adaptive Learning to Filter approach that leverages social signals as well as query-dependent features. To overcome the issue of relevance threshold setting, we use a binary classifier that predicts the relevance of the incoming tweet. In addition, we show the gain that can be achieved by taking advantage of ongoing relevance feedback. Finally, we adopt a real-time push strategy and we show that the proposed approach achieves a promising performance in terms of quality (relevance and novelty) with low cost of latency whereas the state-of-the-art approaches tend to trade latency for higher quality. This thesis also explores a novel approach to generate a retrospective summary that follows a different paradigm than the majority of state-of-the-art methods. We consider the summary generation as an optimization problem that takes into account the topical and the temporal diversity. Tweets are filtered and are incrementally clustered in two cluster types, namely topical clusters based on content similarity and temporal clusters that depends on publication time. Summary generation is formulated as integer linear problem in which unknowns variables are binaries, the objective function is to be maximized and constraints ensure that at most one post per cluster is selected with respect to the defined summary length limit
Nahnsen, Thade. "Automation of summarization evaluation methods and their application to the summarization process". Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5278.
Pełny tekst źródłaSmith, Christian. "Automatic summarization and readability". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68332.
Pełny tekst źródłaSeidlhofer, Barbara. "Discourse analysis for summarization". Thesis, University College London (University of London), 1991. http://discovery.ucl.ac.uk/10018780/.
Pełny tekst źródłaCeylan, Hakan. "Investigating the Extractive Summarization of Literary Novels". Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103298/.
Pełny tekst źródłaDemirtas, Kezban. "Automatic Video Categorization And Summarization". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611113/index.pdf.
Pełny tekst źródłaKsiążki na temat "Summarization"
Automatic summarization. Amsterdam: J. Benjamins Pub. Co., 2001.
Znajdź pełny tekst źródłaTorres-Moreno, Juan-Manuel. Automatic Text Summarization. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.
Pełny tekst źródłaWormeli, Rick. Summarization in Any Subject. Alexandria: ASCD, 2009.
Znajdź pełny tekst źródłaInderjeet, Mani, i Maybury Mark T, red. Advances in automatic text summarization. Cambridge, Mass: MIT Press, 1999.
Znajdź pełny tekst źródłaMehta, Parth, i Prasenjit Majumder. From Extractive to Abstractive Summarization: A Journey. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8934-4.
Pełny tekst źródłaPoibeau, Thierry, Horacio Saggion, Jakub Piskorski i Roman Yangarber, red. Multi-source, Multilingual Information Extraction and Summarization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28569-1.
Pełny tekst źródłaMirkin, Boris. Core Data Analysis: Summarization, Correlation, and Visualization. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00271-8.
Pełny tekst źródłaSeidlhofer, Barbara. Approaches to summarization: Discourse analysis and language education. Tübingen: G. Narr, 1995.
Znajdź pełny tekst źródłaOuyang, Jessica Jin. Adapting Automatic Summarization to New Sources of Information. [New York, N.Y.?]: [publisher not identified], 2019.
Znajdź pełny tekst źródłaMirkin, Boris. Core Concepts in Data Analysis: Summarization, Correlation and Visualization. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-287-2.
Pełny tekst źródłaCzęści książek na temat "Summarization"
Lin, Jimmy. "Summarization". W Encyclopedia of Database Systems, 1–8. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_953-2.
Pełny tekst źródłaLin, Jimmy. "Summarization". W Encyclopedia of Database Systems, 2884–89. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_953.
Pełny tekst źródłaSimske, Steven, i Marie Vans. "Summarization". W Functional Applications of Text Analytics Systems, 35–86. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338222-2.
Pełny tekst źródłaLu, Wen-Jun, i Lei Zhu. "Summarization". W Multi-Mode Resonant Antennas, 251–56. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003291633-7.
Pełny tekst źródłaLin, Jimmy. "Summarization". W Encyclopedia of Database Systems, 3847–54. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_953.
Pełny tekst źródłaTorres-Moreno, Juan-Manuel. "Single-Document Summarization". W Automatic Text Summarization, 53–108. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.ch3.
Pełny tekst źródłaBhatia, Surbhi, Poonam Chaudhary i Nilanjan Dey. "Opinion Summarization". W Opinion Mining in Information Retrieval, 81–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5043-0_6.
Pełny tekst źródłaShen, Dou. "Text Summarization". W Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_424-2.
Pełny tekst źródłaShen, Dou. "Text Summarization". W Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_424-3.
Pełny tekst źródłaBelguith, Lamia Hadrich, Mariem Ellouze, Mohamed Hedi Maaloul, Maher Jaoua, Fatma Kallel Jaoua i Philippe Blache. "Automatic Summarization". W Natural Language Processing of Semitic Languages, 371–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45358-8_12.
Pełny tekst źródłaStreszczenia konferencji na temat "Summarization"
Qiu, Yunjian, i Yan Jin. "Engineering Document Summarization Using Sentence Representations Generated by Bidirectional Language Model". W ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-70866.
Pełny tekst źródłaGoldstein, Jade, i Jaime Carbonell. "Summarization". W a workshop. Morristown, NJ, USA: Association for Computational Linguistics, 1996. http://dx.doi.org/10.3115/1119089.1119120.
Pełny tekst źródłaZhang, Jin, Xueqi Cheng i Hongbo Xu. "Dynamic Summarization: Another Stride Towards Summarization". W 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops. IEEE, 2007. http://dx.doi.org/10.1109/wi-iatw.2007.84.
Pełny tekst źródłaZhang, Jin, Xueqi Cheng i Hongbo Xu. "Dynamic Summarization: Another Stride Towards Summarization". W 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops. IEEE, 2007. http://dx.doi.org/10.1109/wiiatw.2007.4427541.
Pełny tekst źródłaChen, Xiuying, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao i Rui Yan. "Learning towards Abstractive Timeline Summarization". W Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/686.
Pełny tekst źródłaChristensen, Janara, Stephen Soderland, Gagan Bansal i Mausam. "Hierarchical Summarization: Scaling Up Multi-Document Summarization". W Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/p14-1085.
Pełny tekst źródłaKalnikaité, Vaiva, i Steve Whittaker. "Social summarization". W the ACM 2008 conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1460563.1460567.
Pełny tekst źródłaChakraborty, Sunandan, Zohaib Jabbar i Lakshminarayanan Subramanian. "Summarization Search". W ACM DEV '15: Annual Symposium on Computing for Development. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2830629.2835217.
Pełny tekst źródłaLerman, Kevin, i Ryan McDonald. "Contrastive summarization". W Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1620853.1620886.
Pełny tekst źródłaLerman, Kevin, Sasha Blair-Goldensohn i Ryan McDonald. "Sentiment summarization". W the 12th Conference of the European Chapter of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1609067.1609124.
Pełny tekst źródłaRaporty organizacyjne na temat "Summarization"
Tabassi, Elham, i Patrick Grother. Quality summarization :. Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7422.
Pełny tekst źródłaWhite, Michael, Tanya Korelsky, Claire Cardie, Vincent Ng, David Pierce i Kiri Wagstaff. Multidocument Summarization via Information Extraction. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2001. http://dx.doi.org/10.21236/ada457772.
Pełny tekst źródłaFirmin, Therese, i Inderjeet Mani. Automatic Text Summarization in Tipster. Fort Belvoir, VA: Defense Technical Information Center, październik 1998. http://dx.doi.org/10.21236/ada632154.
Pełny tekst źródłaDeMenthon, Daniel, Vikrant Kobla i David Doermann. Video Summarization by Curve Simplification. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1998. http://dx.doi.org/10.21236/ada459300.
Pełny tekst źródłaDe Bock, Jelle, i Steven Verstockt. Automatic Summarization of Cyclocross Races. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317529.
Pełny tekst źródłaSekine, Satoshi, i Chikashi Nobata. A Survey for Multi-Document Summarization. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2003. http://dx.doi.org/10.21236/ada460234.
Pełny tekst źródłaDaume III, Hal, i Daniel Marcu. Generic Sentence Fusion is an Ill-Defined Summarization Task. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2004. http://dx.doi.org/10.21236/ada461416.
Pełny tekst źródłaSiddharthan, Advaith, Ani Nenkova i Kathleen McKeown. Syntactic Simplification for Improving Content Selection in Multi-Document Summarization. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2004. http://dx.doi.org/10.21236/ada457833.
Pełny tekst źródłaKaplin, David B. Automatic Summarization with Sloth (Summarizes Lengthy Documents and Outputs The Highlights). Fort Belvoir, VA: Defense Technical Information Center, listopad 2002. http://dx.doi.org/10.21236/ada408523.
Pełny tekst źródłaDorr, Bonnie, i Terry Gaasterland. Summarization-Inspired Temporal-Relation Extraction: Tense-Pair Templates and Treebank-3 Analysis. Fort Belvoir, VA: Defense Technical Information Center, grudzień 2006. http://dx.doi.org/10.21236/ada460392.
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