Relevant bibliographies by topics / Summarization methods

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  2. Dissertations / Theses
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Academic literature on the topic 'Summarization methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Summarization methods"

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Warkade, Mayank Gangadhar. "Automatic Text Summarization Methods." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 3395–404. https://doi.org/10.22214/ijraset.2025.72807.

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Withtheexponentialincreaseindigitalinformation, the challenge of information overload has become critical. Automatic Text Summarization (ATS) offers a solution by distilling keyinformationfromlargetextsintoconcisesummaries.ThispaperexploresATSmethodologies,focusingonclassificationsbased on input type, purpose, and output type. It provides a detailed analysis of Extractive Text Summarization (ETS), Abstractive Text Summarization (ABS), and Hybrid Text Summarization (HTS). Our implemented ATS system achieves an impressive 90% accuracy, highlighting its effectiveness and reliability. By comparing
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D., K. Kanitha, Muhammad Noorul Mubarak D., and A. Shanavas S. "COMPARISON OF TEXT SUMMARIZER IN INDIAN LANGUAGES." International Journal of Advanced Trends in Engineering and Technology 3, no. 1 (2018): 79–82. https://doi.org/10.5281/zenodo.1205087.

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Text summarization is the process of extracting the relevant information from a source text keeps the significant information. Mainly two types of text summarization methods such as abstractive and extractive. The extractive summarization ranks all sentences and high scored sentences are selected as summary. The abstractive summarization understands the content of a document and re-state in few words. This paper discusses about various text summarization methods followed by the Indian languages. The existing algorithms are explained and then the merits and demerits are discussed. This paper al
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R., Priyadharshini, and Marikkannan M. "A Study Analysis based on Text Summarization Methods." Journal of Advancement in Parallel Computing 4, no. 1 (2021): 1–6. https://doi.org/10.5281/zenodo.4802978.

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Everybody wants the simple and easy to use things in this busy world and then all are expected the ready to serve things in anything. So that same thing also discovered day by day in the computer era, In the computer field the concept of Text summarization creates the most attention. Text summarization is a crucial and timely tool to reduce the text data. This paper produces the main idea of the text summarization, like what is text summarization and also describes what techniques are used in this field currently. Text summarization reduces the huge amount of data into simple text document, it
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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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Peronikolis, Michail, and Costas Panagiotakis. "Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets." Applied Sciences 14, no. 11 (2024): 4400. http://dx.doi.org/10.3390/app14114400.

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In recent years, the scientific and technological developments have led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context,
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Pivovarov, Rimma, and Noémie Elhadad. "Automated methods for the summarization of electronic health records." Journal of the American Medical Informatics Association 22, no. 5 (2015): 938–47. http://dx.doi.org/10.1093/jamia/ocv032.

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Abstract Objectives This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation. Target audience The target audience for this review includes researchers, designers, and informaticians who are concerned about the problem of information overload in the clinical setting as well as both users and developers of clinical summarization systems. Scope Automated summarization has been a lon
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Kirmani, Mahira, Gagandeep Kaur, and Mudasir Mohd. "Analysis of Abstractive and Extractive Summarization Methods." International Journal of Emerging Technologies in Learning (iJET) 19, no. 01 (2024): 86–96. http://dx.doi.org/10.3991/ijet.v19i01.46079.

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This paper explains the existing approaches employed for (automatic) text summarization. The summarizing method is part of the natural language processing (NLP) field and is applied to the source document to produce a compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text-based summarization are categorized into two groups: abstractive and extractive. In abstractive summarization, the main contents of the input text are paraphrased, possibly using vocabulary that is not present in the source document, while in extractive summarization, th
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Upadhye, Akshata. "Unsupervised Document Summarization Using Graph-based Methods." International Journal of Science and Research (IJSR) 10, no. 2 (2021): 1748–52. http://dx.doi.org/10.21275/sr24418110915.

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Rautray, Rasmita, Lopamudra Swain, Rasmita Dash, and Rajashree Dash. "A brief review on text summarization methods." International Journal of Engineering & Technology 7, no. 4.5 (2018): 728. http://dx.doi.org/10.14419/ijet.v7i4.5.25070.

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In present scenario, text summarization is a popular and active field of research in both the Information Retrieval (IR) and Natural Language Processing (NLP) communities. Summarization is important for IR since it is a means to identify useful information by condensing the document from large corpus of data in an efficient way. In this study, different aspects of text summarization methods with strength, limitation and gap within the methods are presented.
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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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Dissertations / Theses on the topic "Summarization methods"

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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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Mieskes, Margot. "Exploring methods for the automatic summarization of meetings." kostenfrei, 2009. http://d-nb.info/99460517X/34.

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Le, Thien-Hoa. "Neural Methods for Sentiment Analysis and Text Summarization." Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0037.

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Cette thèse aborde deux questions majeures du traitement automatique du langage naturel liées à l'analyse sémantique des textes : la détection des sentiments, et le résumé automatique. Dans ces deux applications, la nécessité d'analyser le sens du texte de manière précise est primordiale, d'une part pour identifier le sentiment exprimé au travers des mots, et d'autre part pour extraire les informations saillantes d’une phrase complexe et les réécrire de la manière la plus naturelle possible tout en respectant la sémantique du texte d'origine. Nous abordons ces deux questions par des approches
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Nishino, Masaaki. "Numerical Optimization Methods based on Discrete Structure for Text Summarization and Relational Learning." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/192213.

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Tylenda, Tomasz [Verfasser], and Gerhard [Akademischer Betreuer] Weikum. "Methods and tools for summarization of entities and facts in knowledge bases / Tomasz Tylenda. Betreuer: Gerhard Weikum." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2015. http://d-nb.info/1077211546/34.

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Leite, Daniel Saraiva. "Um estudo comparativo de modelos baseados em estatísticas textuais, grafos e aprendizado de máquina para sumarização automática de textos em português." Universidade Federal de São Carlos, 2010. https://repositorio.ufscar.br/handle/ufscar/459.

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Made available in DSpace on 2016-06-02T19:05:48Z (GMT). No. of bitstreams: 1 3512.pdf: 1897835 bytes, checksum: 598f309a846cb201fe8f13be0f2e37da (MD5) Previous issue date: 2010-12-21<br>Automatic text summarization has been of great interest in Natural Language Processing due to the need of processing a huge amount of information in short time, which is usually delivered through distinct media. Thus, large-scale methods are of utmost importance for synthesizing and making access to information simpler. They aim at preserving relevant content of the sources with little or no human interventio
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Balahur, Dobrescu Alexandra. "Methods and resources for sentiment analysis in multilingual documents of different text types." Doctoral thesis, Universidad de Alicante, 2011. http://hdl.handle.net/10045/19437.

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Jakupovic, Edin. "Alternative Information Gathering on Mobile Devices." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210712.

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Searching and gathering information about specific topics is a time wasting, but vital practise. With the continuous growth and surpassing of desktop devices, the mobile market is becoming a more important area to consider. Due to the portability of mobile devices, certain tasks are more difficult to perform, compared to on a desktop device. Searching for information online is generally slower on mobile devices than on desktop devices, even though the majority of searches are performed on mobile devices. The largest challenges with searching for information online using mobile devices, are the
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Ahmad, Saif. "A temporal pattern identification and summarization method for complex time serial data." Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/843297/.

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Most real-world time series data is produced by complex systems. For example, the economy is a social system which produces time series of stocks, bonds, and foreign exchange rates whereas the human body is a biological system which produces time series of heart rate variations, brain activity, and rate of blood circulation. Complex systems exhibit great variety and complexity and so does the time series emanating from these systems. However, universal principles and tools seem to govern our understanding of highly complex phenomena, processes, and dynamics. It has been argued that one of the
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Cahuina, Edward Jorge Yuri Cayllahua. "A new method for static video summarization using visual words and video temporal segmentation." reponame:Repositório Institucional da UFOP, 2013. http://www.repositorio.ufop.br/handle/123456789/4216.

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Programa de Pós-Graduação em Ciência da Computação. Departamento de Ciência da Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto.<br>Submitted by Oliveira Flávia (flavia@sisbin.ufop.br) on 2015-01-06T17:24:34Z No. of bitstreams: 2 license_rdf: 22190 bytes, checksum: 19e8a2b57ef43c09f4d7071d2153c97d (MD5) DISSERTAÇÃO_NewMethodStatic.pdf: 22958602 bytes, checksum: 14bbececbfb78222b4cef2be60a1d93d (MD5)<br>Approved for entry into archive by Gracilene Carvalho (gracilene@sisbin.ufop.br) on 2015-01-15T17:23:20Z (GMT) No. of bitstreams: 2 license_rdf: 22190 b
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Books on the topic "Summarization methods"

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Kedzie, Christopher. Salience Estimation and Faithful Generation: Modeling Methods for Text Summarization and Generation. [publisher not identified], 2021.

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Jöreskog, K. G. PRELIS 2 user's reference guide: A program for multivariate data screening and data summarization : a preprocessor for LISREL. Scientific Software International, 2002.

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Hovy, Eduard. Text Summarization. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0032.

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This article describes research and development on the automated creation of summaries of one or more texts. It defines the concept of summary and presents an overview of the principal approaches in summarization. It describes the design, implementation, and performance of various summarization systems. The stages of automated text summarization are topic identification, interpretation, and summary generation, each having its sub stages. Due to the challenges involved, multi-document summarization is much less developed than single-document summarization. This article reviews particular techni
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Innovative Document Summarization Techniques Revolutionizing Knowledge Understanding. Idea Group,U.S., 2014.

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Core concepts in data analysis: Summarization, correlation and visualization. Springer, 2011.

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Hirschman, Lynette, and Inderjeet Mani. Evaluation. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0022.

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The commercial success of natural language (NL) technology has raised the technical criticality of evaluation. Choices of evaluation methods depend on software life cycles, typically charting four stages — research, advance prototype, operational prototype, and product. At the prototype stage, embedded evaluation can prove helpful. Analysis components can be loose grouped viz., segmentation, tagging, extracting information, and document threading. Output technologies such as text summarization can be evaluated in terms of intrinsic and extrinsic measures, the former checking for quality and in
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Book chapters on the topic "Summarization methods"

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Nahhas, Ramzi W. "Data Summarization." In Introduction to Regression Methods for Public Health Using R. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003263197-3.

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Yadav, Vandana, Jon Atle Gulla, Özlem Özgöbek, and Lemei Zhang. "News Timeline Summarization: Recent Methods." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97141-9_25.

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Khan, Sarim, Abhay Pathak, Rishabh Chopra, Hemant Singh Parihar, and Preet Chandan Kaur. "Study of Various Text Summarization Methods." In Artificial Intelligence: Theory and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8476-3_10.

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Menéndez, Héctor D., and Aidan Dakhama. "Automatic Summarization Evaluation: Methods and Practices." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80889-0_12.

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Varghese, Tiju George, and C. V. Priya. "Automatic Text Summarization: Methods, Metrics and Datasets." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8398-8_6.

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Baruah, Nomi, Shikhar Kr Sarma, Surajit Borkotokey, Randeep Borah, Rakhee D. Phukan, and Arjun Gogoi. "A Graph-Based Extractive Assamese Text Summarization." In Computational Methods and Data Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3015-7_1.

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Driscoll, Patricia. "Computational Methods for Name Normalization Using Hypocoristic Personal Name Variants." In Multi-source, Multilingual Information Extraction and Summarization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28569-1_4.

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Marangoz, Semih, and Ahmet Sayar. "Comparison of Text Summarization Methods in Turkish Texts." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56728-5_27.

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Afantenos, Stergos D., Irene Doura, Eleni Kapellou, and Vangelis Karkaletsis. "Exploiting Cross-Document Relations for Multi-document Evolving Summarization." In Methods and Applications of Artificial Intelligence. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24674-9_43.

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Lenka, Ranjita Kumari Biswal, Thomas Coombs, Sulaf Assi, et al. "Evaluation of Extractive and Abstract Methods in Text Summarization." In Data Science and Emerging Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0741-0_38.

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Conference papers on the topic "Summarization methods"

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Mhatre, Sonali, and Lata Ragha. "Implementing Extractive Summarization Methods on Extractive Datasets." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810786.

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Li, Chen, Fei Liu, Fuliang Weng, and Yang Liu. "Document Summarization via Guided Sentence Compression." In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/d13-1047.

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Forde, Jessica Zosa, Ruochen Zhang, Lintang Sutawika, et al. "Re-Evaluating Evaluation for Multilingual Summarization." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.1085.

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Subbiah, Melanie, Faisal Ladhak, Akankshya Mishra, Griffin Thomas Adams, Lydia Chilton, and Kathleen McKeown. "STORYSUMM: Evaluating Faithfulness in Story Summarization." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.557.

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Wang, Zhongqing, Shoushan Li, Fang Kong, and Guodong Zhou. "Collective Personal Profile Summarization with Social Networks." In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/d13-1067.

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Luthra, Julie, and Priti Maheshwary. "A Survey of Advances in Text Summarization Methods." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743404.

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Afsharizadeh, Mahsa, and Seyed Jalaleddin Mousavirad. "A Survey on Transformer-Based Extractive Summarization Methods." In 2024 19th Iranian Conference on Intelligent Systems (ICIS). IEEE, 2024. https://doi.org/10.1109/icis64839.2024.10887480.

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Alsekait, Deema mohammed, Waref Almanaseer, Ibrahim A. Gomaa, et al. "Harnessing Deep Learning for Effective Extractive Text Summarization: A Comparative Study." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652647.

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Hsu, Chao-Chun, and Chenhao Tan. "Decision-Focused Summarization." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.10.

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Barbieri, Mauro, Lalitha Agnihotri, and Nevenka Dimitrova. "Video summarization: methods and landscape." In ITCom 2003, edited by John R. Smith, Sethuraman Panchanathan, and Tong Zhang. SPIE, 2003. http://dx.doi.org/10.1117/12.515733.

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