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Relevant bibliographies by topics / Summarization

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Academic literature on the topic 'Summarization'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Summarization"

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da Cunha, Iria, Leo Wanner, and Teresa Cabré. "Summarization of specialized discourse." Terminology 13, no. 2 (2007): 249–86. http://dx.doi.org/10.1075/term.13.2.07cun.

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In this article, we present the current state of our work on a linguistically-motivated model for automatic summarization of medical articles in Spanish. The model takes into account the results of an empirical study which reveals that, on the one hand, domain-specific summarization criteria can often be derived from the summaries of domain specialists, and, on the other hand, adequate summarization strategies must be multidimensional, i.e., cover various types of linguistic clues. We take into account the textual, lexical, discursive, syntactic and communicative dimensions. This is novel in t

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Sirohi, Neeraj Kumar, Dr Mamta Bansal, and Dr S. N. Rajan Rajan. "Text Summarization Approaches Using Machine Learning & LSTM." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 5010–26. http://dx.doi.org/10.47059/revistageintec.v11i4.2526.

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Due to the massive amount of online textual data generated in a diversity of social media, web, and other information-centric applications. To select the vital data from the large text, need to study the full article and generate summary also not loose critical information of text document this process is called summarization. Text summarization is done either by human which need expertise in that area, also very tedious and time consuming. second type of summarization is done through system which is known as automatic text summarization which generate summary automatically. There are mainly t

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Blekanov, Ivan S., Nikita Tarasov, and Svetlana S. Bodrunova. "Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages." Future Internet 14, no. 3 (2022): 69. http://dx.doi.org/10.3390/fi14030069.

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Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual a

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Pei, Jisheng, and Xiaojun Ye. "Information-Balance-Aware Approximated Summarization of Data Provenance." Scientific Programming 2017 (September 12, 2017): 1–11. http://dx.doi.org/10.1155/2017/4504589.

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Extracting useful knowledge from data provenance information has been challenging because provenance information is often overwhelmingly enormous for users to understand. Recently, it has been proposed that we may summarize data provenance items by grouping semantically related provenance annotations so as to achieve concise provenance representation. Users may provide their intended use of the provenance data in terms of provisioning, and the quality of provenance summarization could be optimized for smaller size and closer distance between the provisioning results derived from the summarizat

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Li, Chih-Yuan, Soon Ae Chun, and James Geller. "Perspective-Based Microblog Summarization." Information 16, no. 4 (2025): 285. https://doi.org/10.3390/info16040285.

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Social media allows people to express and share a variety of experiences, opinions, beliefs, interpretations, or viewpoints on a single topic. Summarizing a collection of social media posts (microblogs) on one topic may be challenging and can result in an incoherent summary due to multiple perspectives from different users. We introduce a novel approach to microblog summarization, the Multiple-View Summarization Framework (MVSF), designed to efficiently generate multiple summaries from the same social media dataset depending on chosen perspectives and deliver personalized and fine-grained summ

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M., Nafees Muneera, and P.Sriramya. "Extractive Text Summarization for Social News using Hybrid Techniques in Opinion Mining." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2109–15. https://doi.org/10.35940/ijeat.B3356.029320.

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Presently almost all enterprises are oriented into building text data in abundance savoring the benefits of big data concept but the reality is that it’s not practically possible to go through all this data/documents for decision making because of the time constraint. Here in exists intense need of an approach as an alternative for the actual content which can summarize the complete textual content. By adopting these summarizing approaches, the accuracy in data retrieval of summarized content via search queries can be enhanced compared to performing search over the broad range of origina

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Bhatia, Neelima, and Arunima Jaiswal. "Literature Review on Automatic Text Summarization: Single and Multiple Summarizations." International Journal of Computer Applications 117, no. 6 (2015): 25–29. http://dx.doi.org/10.5120/20560-2948.

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Zhang, Qianjin, Dahai Jin, Yawen Wang, and Yunzhan Gong. "Statement-Grained Hierarchy Enhanced Code Summarization." Electronics 13, no. 4 (2024): 765. http://dx.doi.org/10.3390/electronics13040765.

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Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features, causing limited code representation. Addressing this gap, our paper introduces the statement-grained hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets.

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S, Sai Shashank, Sindhu S, Vineeth V, and Pranathi C. "VIDEO SUMMARIZATION." International Research Journal of Computer Science 9, no. 8 (2022): 277–80. http://dx.doi.org/10.26562/irjcs.2022.v0908.24.

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The general public now has access to a vast amount of multimedia information thanks to recent technological advancements and the quick expansion of consumer electronics, making it challenging to effectively consume video material among the thousands of options accessible. By choosing and presenting the most educational or fascinating materials for users, we provide a method to quickly summarize the content of a lengthy video document. The practice of condensing a raw video into a more manageable form without losing much information is known as video summarizing. Either a comprehensive analysis

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Nenkova, Ani. "Automatic Summarization." Foundations and Trends® in Information Retrieval 5, no. 2 (2011): 103–233. http://dx.doi.org/10.1561/1500000015.

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Dissertations / Theses on the topic "Summarization"

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Bosma, Wauter Eduard. "Discourse oriented summarization." Enschede : Centre for Telematics and Information Technology (CTIT), 2008. http://doc.utwente.nl/58836.

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Moon, Brandon B. "Interactive football summarization /." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3337.pdf.

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Moon, Brandon B. "Interactive Football Summarization." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1999.

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Football fans do not have the time to watch every game in its entirety and need an effective solution that summarizes them the story of the game. Human-generated summaries are often too short, requiring time and resources to create. We utilize the advantages of Interactive TV to create an automatic football summarization service that is cohesive, provides context, covers the necessary plays, and is concise. First, we construct a degree of interest function that ranks each play based on detailed, play-by-play game events as well as viewing statistics collected from an interactive viewing enviro

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Sizov, 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.

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<p>A 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. T

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Chellal, Abdelhamid. "Event summarization on social media stream : retrospective and prospective tweet summarization." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30118/document.

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Le contenu généré dans les médias sociaux comme Twitter permet aux utilisateurs d'avoir un aperçu rétrospectif d'évènement et de suivre les nouveaux développements dès qu'ils se produisent. Cependant, bien que Twitter soit une source d'information importante, il est caractérisé par le volume et la vélocité des informations publiées qui rendent difficile le suivi de l'évolution des évènements. Pour permettre de mieux tirer profit de ce nouveau vecteur d'information, deux tâches complémentaires de recherche d'information dans les médias sociaux ont été introduites : la génération de résumé rétro

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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.

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Summarization is the process of creating a more compact textual representation of a document or a collection of documents. In view of the vast increase in electronically available information sources in the last decade, filters such as automatically generated summaries are becoming ever more important to facilitate the efficient acquisition and use of required information. Different methods using natural language processing (NLP) techniques are being used to this end. One of the shallowest approaches is the clustering of available documents and the representation of the resulting clusters by o

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Smith, Christian. "Automatic summarization and readability." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68332.

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The enormous amount of information available today within different media gives rise to the notion of ways to reduce the inevitable complexity and to distribute text material to different channels or media. In an effort to investigate the possibilities of a tool to help eleviate the problem, an automatic summarizer called COGSUM has been developed and evaluated with regards to the informational quality of the summaries and with regards to the readability. COGSUM is based on word space methodology, including virtues such as problematic computational complexity and possibilities of inferring sem

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Seidlhofer, Barbara. "Discourse analysis for summarization." Thesis, University College London (University of London), 1991. http://discovery.ucl.ac.uk/10018780/.

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Summarization is an activity which language students are frequently called upon to perform, often without any explicit guidance. In a wider sense, it might be said that all learning, whether of language or anything else, involves the ability to distinguish what is important from what is not, and to incorporate it into existing schematic knowledge. In this respect, summarization can be seen as central to education in general as well as language education in particular. This thesis is an attempt to gain insights into the essential criteria for summarization. After the first chapter has outlined

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Ceylan, Hakan. "Investigating the Extractive Summarization of Literary Novels." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103298/.

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Abstract Due to the vast amount of information we are faced with, summarization has become a critical necessity of everyday human life. Given that a large fraction of the electronic documents available online and elsewhere consist of short texts such as Web pages, news articles, scientific reports, and others, the focus of natural language processing techniques to date has been on the automation of methods targeting short documents. We are witnessing however a change: an increasingly larger number of books become available in electronic format. This means that the need for language process

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Demirtas, Kezban. "Automatic Video Categorization And Summarization." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611113/index.pdf.

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In this thesis, we make automatic video categorization and summarization by using subtitles of videos. We propose two methods for video categorization. The first method makes unsupervised categorization by applying natural language processing techniques on video subtitles and uses the WordNet lexical database and WordNet domains. The method starts with text preprocessing. Then a keyword extraction algorithm and a word sense disambiguation method are applied. The WordNet domains that correspond to the correct senses of keywords are extracted. Video is assigned a category label based on the extr

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Books on the topic "Summarization"

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Torres-Moreno, Juan-Manuel. Automatic Text Summarization. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.

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Wormeli, Rick. Summarization in Any Subject. ASCD, 2009.

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Inderjeet, Mani, and Maybury Mark T, eds. Advances in automatic text summarization. MIT Press, 1999.

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Mehta, Parth, and Prasenjit Majumder. From Extractive to Abstractive Summarization: A Journey. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8934-4.

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Poibeau, Thierry, Horacio Saggion, Jakub Piskorski, and Roman Yangarber, eds. Multi-source, Multilingual Information Extraction and Summarization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28569-1.

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Mirkin, Boris. Core Data Analysis: Summarization, Correlation, and Visualization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00271-8.

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Seidlhofer, Barbara. Approaches to summarization: Discourse analysis and language education. G. Narr, 1995.

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Petinot, Yves. A Nearest-Neighbor Approach to Indicative Web Summarization. [publisher not identified], 2016.

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Ouyang, Jessica Jin. Adapting Automatic Summarization to New Sources of Information. [publisher not identified], 2019.

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Mirkin, Boris. Core Concepts in Data Analysis: Summarization, Correlation and Visualization. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-287-2.

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Book chapters on the topic "Summarization"

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Lin, Jimmy. "Summarization." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_953-2.

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Lin, Jimmy. "Summarization." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_953.

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Simske, Steven, and Marie Vans. "Summarization." In Functional Applications of Text Analytics Systems. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338222-2.

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Pulapaka, Sanjeev, Srinath Godavarthi, and Dr Sherry Ding. "Summarization." In Empowering the Public Sector with Generative AI. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0473-1_7.

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Lu, Wen-Jun, and Lei Zhu. "Summarization." In Multi-Mode Resonant Antennas. CRC Press, 2022. http://dx.doi.org/10.1201/9781003291633-7.

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Lin, Jimmy. "Summarization." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_953.

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Torres-Moreno, Juan-Manuel. "Single-Document Summarization." In Automatic Text Summarization. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.ch3.

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Torres-Moreno, Juan-Manuel. "Why Summarize Texts?" In Automatic Text Summarization. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.ch1.

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Torres-Moreno, Juan-Manuel. "Automatic Text Summarization: Some Important Concepts." In Automatic Text Summarization. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.ch2.

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Torres-Moreno, Juan-Manuel. "Guided Multi-Document Summarization." In Automatic Text Summarization. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119004752.ch4.

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Conference papers on the topic "Summarization"

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Gantt, William, Alexander Martin, Pavlo Kuchmiichuk, and Aaron Steven White. "Event-Keyed Summarization." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.431.

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Gigi, Deepak Tom, Lisa James, Meenakshi B S, Muhammed Afreen K N, and Anjaly Muralidharan. "Dual Approach for Video Summarization: Key Frame Captioning and Audio Summarization." In 2025 2nd International Conference on Trends in Engineering Systems and Technologies (ICTEST). IEEE, 2025. https://doi.org/10.1109/ictest64710.2025.11042834.

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Qiu, Yunjian, and Yan Jin. "Engineering Document Summarization Using Sentence Representations Generated by Bidirectional Language Model." In 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.

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Abstract In this study, the extractive summarization using sentence embeddings generated by the finetuned BERT (Bidirectional Encoder Representations from Transformers) models and the K-Means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific datasets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then app

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Goldstein, Jade, and Jaime Carbonell. "Summarization." In a workshop. Association for Computational Linguistics, 1996. http://dx.doi.org/10.3115/1119089.1119120.

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Zhang, Jin, Xueqi Cheng, and Hongbo Xu. "Dynamic Summarization: Another Stride Towards Summarization." In 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.

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Zhang, Jin, Xueqi Cheng, and Hongbo Xu. "Dynamic Summarization: Another Stride Towards Summarization." In 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.

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Chen, Xiuying, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, and Rui Yan. "Learning towards Abstractive Timeline Summarization." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/686.

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Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods.In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events.Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order.To tackle this challenge, we propose a memory-based timeline summarization model

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Christensen, Janara, Stephen Soderland, Gagan Bansal, and Mausam. "Hierarchical Summarization: Scaling Up Multi-Document Summarization." In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/p14-1085.

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Kalnikaité, Vaiva, and Steve Whittaker. "Social summarization." In the ACM 2008 conference. ACM Press, 2008. http://dx.doi.org/10.1145/1460563.1460567.

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Chakraborty, Sunandan, Zohaib Jabbar, and Lakshminarayanan Subramanian. "Summarization Search." In ACM DEV '15: Annual Symposium on Computing for Development. ACM, 2015. http://dx.doi.org/10.1145/2830629.2835217.

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Reports on the topic "Summarization"

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Tabassi, Elham, and Patrick Grother. Quality summarization :. National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7422.

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White, Michael, Tanya Korelsky, Claire Cardie, Vincent Ng, David Pierce, and Kiri Wagstaff. Multidocument Summarization via Information Extraction. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada457772.

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Firmin, Therese, and Inderjeet Mani. Automatic Text Summarization in Tipster. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada632154.

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DeMenthon, Daniel, Vikrant Kobla, and David Doermann. Video Summarization by Curve Simplification. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada459300.

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De Bock, Jelle, and Steven Verstockt. Automatic Summarization of Cyclocross Races. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317529.

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Sekine, Satoshi, and Chikashi Nobata. A Survey for Multi-Document Summarization. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada460234.

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Daume III, Hal, and Daniel Marcu. Generic Sentence Fusion is an Ill-Defined Summarization Task. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada461416.

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Siddharthan, Advaith, Ani Nenkova, and Kathleen McKeown. Syntactic Simplification for Improving Content Selection in Multi-Document Summarization. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada457833.

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Link, Hamilton, and Adam Hooker. A Summary of Advances in Document Summarization from 2023-2024. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2462994.

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Kaplin, David B. Automatic Summarization with Sloth (Summarizes Lengthy Documents and Outputs The Highlights). Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada408523.

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