Готові списки джерел за темами / SUMMARIZATION ALGORITHMS

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Добірка наукової літератури з теми "SUMMARIZATION ALGORITHMS"

Автор: Grafiati

Опубліковано: 11 вересня 2023

Оновлено: 31 липня 2025

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Статті в журналах з теми "SUMMARIZATION ALGORITHMS"

Chang, Hsien-Tsung, Shu-Wei Liu, and Nilamadhab Mishra. "A tracking and summarization system for online Chinese news topics." Aslib Journal of Information Management 67, no. 6 (2015): 687–99. http://dx.doi.org/10.1108/ajim-10-2014-0147.

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Анотація:

Purpose – The purpose of this paper is to design and implement new tracking and summarization algorithms for Chinese news content. Based on the proposed methods and algorithms, the authors extract the important sentences that are contained in topic stories and list those sentences according to timestamp order to ensure ease of understanding and to visualize multiple news stories on a single screen. Design/methodology/approach – This paper encompasses an investigational approach that implements a new Dynamic Centroid Summarization algorithm in addition to a Term Frequency (TF)-Density algorithm

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Et. al., Tamilselvan Jayaraman,. "Brainstorm optimization for multi-document summarization." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 7607–19. http://dx.doi.org/10.17762/turcomat.v12i10.5670.

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Document summarization is one of the solutions to mine the appropriate information from a huge number of documents. In this study, brainstorm optimization (BSO) based multi-document summarizer (MDSBSO) is proposed to solve the problem of multi-document summarization. The proposed MDSBSO is compared with two other multi-document summarization algorithms including particle swarm optimization (PSO) and bacterial foraging optimization (BFO). To evaluate the performance of proposed multi-document summarizer, two well-known benchmark document understanding conference (DUC) datasets are used. Perform

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Mall, Shalu, Avinash Maurya, Ashutosh Pandey, and Davain Khajuria. "Centroid Based Clustering Approach for Extractive Text Summarization." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3404–9. http://dx.doi.org/10.22214/ijraset.2023.53542.

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Abstract: Extractive text summarization is the process of identifying the most important information from a large text and presenting it in a condensed form. One popular approach to this problem is the use of centroid-based clustering algorithms, which group together similar sentences based on their content and then select representative sentences from each cluster to form a summary. In this research, we present a centroid-based clustering algorithm for email summarization that combines the use of word embeddings with a clustering algorithm. We compare our algorithm to existing summarization t

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Yadav, Divakar, Naman Lalit, Riya Kaushik, et al. "Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain." Computational Intelligence and Neuroscience 2022 (February 9, 2022): 1–14. http://dx.doi.org/10.1155/2022/3411881.

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Анотація:

For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method. Manual summarization by experts is an almost impossible and time-consuming activity. People could not access, read, or use such a big pile of information for their needs. Therefore, summary generation is essential and beneficial in the current scenario. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. We implemented five different algorithms, namely, term frequency-inverse document

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BOKAEI, MOHAMMAD HADI, HOSSEIN SAMETI, and YANG LIU. "Extractive summarization of multi-party meetings through discourse segmentation." Natural Language Engineering 22, no. 1 (2015): 41–72. http://dx.doi.org/10.1017/s1351324914000199.

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AbstractIn this article we tackle the problem of multi-party conversation summarization. We investigate the role of discourse segmentation of a conversation on meeting summarization. First, an unsupervised function segmentation algorithm is proposed to segment the transcript into functionally coherent parts, such asMonologuei(which indicates a segment where speakeriis the dominant speaker, e.g., lecturing all the other participants) orDiscussionx1x2, . . .,xn(which indicates a segment where speakersx1toxninvolve in a discussion). Then the salience score for a sentence is computed by leveraging

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Ioannis, Mademlis, Tefas Anastasios, and Pitas Ioannis. "A salient dictionary learning framework for activity video summarization via key-frame extraction." Elsevier Information Sciences 432 (January 2, 2018): 319–31. https://doi.org/10.1016/j.ins.2017.12.020.

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Recently, dictionary learning methods for unsupervised video summarization have surpassed traditional video frame clustering approaches. This paper addresses static summarization of videos depicting activities, which possess certain recurrent properties. In this context, a flexible definition of an activity video summary is proposed, as the set of key-frames that can both reconstruct the original, full-length video and simultaneously represent its most salient parts. Both objectives can be jointly optimized across several information modalities. The two criteria are merged into a “salien

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Dutta, Soumi, Vibhash Chandra, Kanav Mehra, Asit Kumar Das, Tanmoy Chakraborty, and Saptarshi Ghosh. "Ensemble Algorithms for Microblog Summarization." IEEE Intelligent Systems 33, no. 3 (2018): 4–14. http://dx.doi.org/10.1109/mis.2018.033001411.

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Chu, Deming, Fan Zhang, Wenjie Zhang, Ying Zhang, and Xuemin Lin. "Graph Summarization: Compactness Meets Efficiency." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–26. http://dx.doi.org/10.1145/3654943.

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Анотація:

As the volume and ubiquity of graphs increase, a compact graph representation becomes essential for enabling efficient storage, transfer, and processing of graphs. Given a graph, the graph summarization problem asks for a compact representation that consists of a summary graph and the corrections, such that we can recreate the original graph from the representation exactly. Although this problem has been studied extensively, the existing works either trade summary compactness for efficiency, or vice versa. In particular, a well-known greedy method provides the most compact summary but incurs p

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Han, Kai, Shuang Cui, Tianshuai Zhu, et al. "Approximation Algorithms for Submodular Data Summarization with a Knapsack Constraint." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (2022): 65–66. http://dx.doi.org/10.1145/3543516.3453922.

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Анотація:

Data summarization, a fundamental methodology aimed at selecting a representative subset of data elements from a large pool of ground data, has found numerous applications in big data processing, such as social network analysis [5, 7], crowdsourcing [6], clustering [4], network design [13], and document/corpus summarization [14]. Moreover, it is well acknowledged that the "representativeness" of a dataset in data summarization applications can often be modeled by submodularity - a mathematical concept abstracting the "diminishing returns" property in the real world. Therefore, a lot of studies

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Han, Kai, Shuang Cui, Tianshuai Zhu, et al. "Approximation Algorithms for Submodular Data Summarization with a Knapsack Constraint." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 1 (2021): 1–31. http://dx.doi.org/10.1145/3447383.

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Анотація:

Data summarization, i.e., selecting representative subsets of manageable size out of massive data, is often modeled as a submodular optimization problem. Although there exist extensive algorithms for submodular optimization, many of them incur large computational overheads and hence are not suitable for mining big data. In this work, we consider the fundamental problem of (non-monotone) submodular function maximization with a knapsack constraint, and propose simple yet effective and efficient algorithms for it. Specifically, we propose a deterministic algorithm with approximation ratio 6 and a

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Дисертації з теми "SUMMARIZATION ALGORITHMS"

Kolla, Maheedhar, and 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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Summarization is a complex task that requires understanding of the document content to determine the importance of the text. Lexical cohesion is a method to identify connected portions of the text based on the relations between the words in the text. Lexical cohesive relations can be represented using lexical chaings. Lexical chains are sequences of semantically related words spread over the entire text. Lexical chains are used in variety of Natural Language Processing (NLP) and Information Retrieval (IR) applications. In current thesis, we propose a lexical chaining method that includes the g

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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.

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Hamid, 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/.

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Анотація:

Automatic text summarization and keyphrase extraction are two interesting areas of research which extend along natural language processing and information retrieval. They have recently become very popular because of their wide applicability. Devising generic techniques for these tasks is challenging due to several issues. Yet we have a good number of intelligent systems performing the tasks. As different systems are designed with different perspectives, evaluating their performances with a generic strategy is crucial. It has also become immensely important to evaluate the performances with min

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Chiarandini, Luca. "Characterizing and modeling web sessions with applications." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/283414.

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This thesis focuses on the analysis and modeling of web sessions, groups of requests made by a single user for a single navigation purpose. Understanding how people browse through websites is important, helping us to improve interfaces and provide to better content. After first conducting a statistical analysis of web sessions, we go on to present algorithms to summarize and model web sessions. Finally, we describe applications that use novel browsing methods, in particular parallel browsing. We observe that people tend to browse images in a sequences and that those sequences can be co

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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/.

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Анотація:

O agrupamento de dados (ou do inglês Clustering) é uma tarefa não supervisionada capaz de descrever objetos em grupos (ou clusters), de maneira que objetos de um mesmo grupo sejam mais semelhantes entre si do que objetos de grupos distintos. As técnicas de agrupamento de dados são divididas em duas principais categorias: particionais e hierárquicas. As técnicas particionais dividem um conjunto de dados em um determinado número de grupos distintos, enquanto as técnicas hierárquicas fornecem uma sequência aninhada de agrupamentos particionais separados por diferentes níveis de granularidade. Adi

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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.

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Анотація:

Obwohl schon seit den 50er Jahren auf dem Gebiet der Automatischen Textzusammenfassung Forschung betrieben wird, wurden der Nutzen und die Notwendigkeit dieser Systeme erst mit dem Boom des Internets richtig erkannt. Das World Wide Web stellt eine täglich wachsende Menge an Informationen zu nahezu jedem Thema zur Verfügung. Um den Zeitaufwand zum Finden und auch zum Wiederfinden der richtigen Informationen zu minimieren, traten Suchmaschinen ihren Siegeszug an. Doch um einen Überblick zu einem ausgewählten Thema zu erhalten, ist eine einfache Auflistung aller in Frage kommenden Seiten nicht

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Maaloul, Mohamed. "Approche hybride pour le résumé automatique de textes : Application à la langue arabe." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4778.

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Анотація:

Cette thèse s'intègre dans le cadre du traitement automatique du langage naturel. La problématique du résumé automatique de documents arabes qui a été abordée, dans cette thèse, s'est cristallisée autour de deux points. Le premier point concerne les critères utilisés pour décider du contenu essentiel à extraire. Le deuxième point se focalise sur les moyens qui permettent d'exprimer le contenu essentiel extrait sous la forme d'un texte ciblant les besoins potentiels d'un utilisateur. Afin de montrer la faisabilité de notre approche, nous avons développé le système "L.A.E", basé sur une approche

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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.

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This thesis deals with one-document summarization of text data. Part of it is devoted to data preparation, mainly to the normalization. Listed are some of the stemming algorithms and it contains also description of lemmatization. The main part is devoted to Luhn"s method for summarization and its extension of use WordNet dictionary. Oswald summarization method is described and applied as well. Designed and implemented application performs automatic generation of abstracts using these methods. A set of experiments where developed, which verified correct functionality of the application and of e

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Hassanlou, Nasrin. "Probabilistic graph summarization." Thesis, 2012. http://hdl.handle.net/1828/4403.

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Анотація:

We study group-summarization of probabilistic graphs that naturally arise in social networks, semistructured data, and other applications. Our proposed framework groups the nodes and edges of the graph based on a user selected set of node attributes. We present methods to compute useful graph aggregates without the need to create all of the possible graph-instances of the original probabilistic graph. Also, we present an algorithm for graph summarization based on pure relational (SQL) technology. We analyze our algorithm and practically evaluate its efficiency using an extended Epinions

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SINGH, SWATI. "ANALYSIS FOR TEXT SUMMARIZATION ALGORITHMS FOR DIFFERENT DATASETS." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15975.

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Анотація:

With the exponential increase in the data available on the internet for a single domain, it is difficult to understand the gist of a whole document without reading the whole document. Automatic Text Summarization reduces the content of the document by presenting important key points from the data. Extracting the major points from the document is easier and requires less machinery than forming new sentences from the available data. Research in this domain started nearly 50 years ago from identifying key features to rank important sentences in a text document. The main aim of text summariz

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Частини книг з теми "SUMMARIZATION ALGORITHMS"

Tian, Yuanyuan, and Jignesh M. Patel. "Interactive Graph Summarization." In Link Mining: Models, Algorithms, and Applications. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6515-8_15.

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Javed, Hira, M. M. Sufyan Beg, and Nadeem Akhtar. "Multimodal Summarization: A Concise Review." In Algorithms for Intelligent Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6893-7_54.

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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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Komorowski, Artur, Lucjan Janowski, and Mikołaj Leszczuk. "Evaluation of Multimedia Content Summarization Algorithms." In Cryptology and Network Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98678-4_43.

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Zhao, Yu, Songping Huang, Dongsheng Zhou, Zhaoyun Ding, Fei Wang, and Aixin Nian. "CNsum: Automatic Summarization for Chinese News Text." In Wireless Algorithms, Systems, and Applications. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19214-2_45.

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Sharma, Arjun Datt, and Shaleen Deep. "Too Long-Didn’t Read: A Practical Web Based Approach towards Text Summarization." In Applied Algorithms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04126-1_17.

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Gokul Amuthan, S., and S. Chitrakala. "CESumm: Semantic Graph-Based Approach for Extractive Text Summarization." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3246-4_8.

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Chen, Chen, Cindy Xide Lin, Matt Fredrikson, Mihai Christodorescu, Xifeng Yan, and Jiawei Han. "Mining Large Information Networks by Graph Summarization." In Link Mining: Models, Algorithms, and Applications. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6515-8_18.

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Tsitovich, Aliaksei, Natasha Sharygina, Christoph M. Wintersteiger, and Daniel Kroening. "Loop Summarization and Termination Analysis." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19835-9_9.

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Rehman, Tohida, Suchandan Das, Debarshi Kumar Sanyal, and Samiran Chattopadhyay. "An Analysis of Abstractive Text Summarization Using Pre-trained Models." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1657-1_21.

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Тези доповідей конференцій з теми "SUMMARIZATION ALGORITHMS"

More, Mukesh, Pallavi Yevale, Abhang Mandwale, Kundan Agrawal, Om Mahale, and Sahilsing Rajput. "Hindi Text Summarization: Using BERT." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721619.

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Ganguly, Sayam, Sourav Mandal, Nabanita Das, Bikash Sadhukhan, Sagarika Sarkar, and Swagata Paul. "WhisperSum: Unified Audio-to-Text Summarization." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721926.

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wang, kang, guohua shen, zhiqiu huang, and xinbo zhang. "Exploring ChatGPT's code summarization capabilities: an empirical study." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051717.

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Sun, Dan, Jacky He, Hanlu Zhang, Zhen Qi, Hongye Zheng, and Xiaokai Wang. "A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019176.

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Chen, Yongming. "Data Pyramid for Enterprise News Summarization: A Three-Stage Knowledge-Enhanced Approach." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019188.

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Nugroho, Galang Setia, Esmeralda Contessa Dj amal, and Ridwan Ilyas. "Summarization of Scientific Articles using Hybrid SciBERT and Graph-Based Algorithms." In 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2024. https://doi.org/10.1109/eecsi63442.2024.10776308.

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Chakraborti, Rounak, Romit Banerjee, and Soma Das. "Evaluating the Efficacy of Text Summarization Models: A Comparison of NLP Algorithms." In 2025 8th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2025. https://doi.org/10.1109/iementech65115.2025.10959463.

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Muhamediyeva, Dilnoz, Nilufar Niyozmatova, Sanjar Ungalov, Mamatov Abduvali, and Turgunova Nafisa. "Evaluating the effectiveness of text summarization algorithms based on recall-oriented understudy for Gisting evaluation metrics." In Fourth International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2025), edited by Arthur Gibadullin and Khamza Eshankulov. SPIE, 2025. https://doi.org/10.1117/12.3072740.

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R, Sandhya B., Shusank Chaudhary, Basant Pandit, Khem Raj Seth, and Aditya Jha. "Unleashing the potential of Natural Language Processing for News Link Article Summarization: Comparing TF-IDF and Text Rank Algorithms." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10939237.

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Ren, Zheng. "Balancing role contributions: a novel approach for role-oriented dialogue summarization." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039616.

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