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Статті в журналах з теми "SUMMARIZATION ALGORITHMS"

Автор: Grafiati

Опубліковано: 11 вересня 2023

Оновлено: 31 липня 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Popescu, Claudiu, Lacrimioara Grama, and Corneliu Rusu. "A Highly Scalable Method for Extractive Text Summarization Using Convex Optimization." Symmetry 13, no. 10 (2021): 1824. http://dx.doi.org/10.3390/sym13101824.

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Анотація:

The paper describes a convex optimization formulation of the extractive text summarization problem and a simple and scalable algorithm to solve it. The optimization program is constructed as a convex relaxation of an intuitive but computationally hard integer programming problem. The objective function is highly symmetric, being invariant under unitary transformations of the text representations. Another key idea is to replace the constraint on the number of sentences in the summary with a convex surrogate. For solving the program we have designed a specific projected gradient descent algorith

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12

Boussaid, L., A. Mtibaa, M. Abid, and M. Paindavoin. "Real-Time Algorithms for Video Summarization." Journal of Applied Sciences 6, no. 8 (2006): 1679–85. http://dx.doi.org/10.3923/jas.2006.1679.1685.

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13

Silber, H. Gregory, and Kathleen F. McCoy. "Efficiently Computed Lexical Chains as an Intermediate Representation for Automatic Text Summarization." Computational Linguistics 28, no. 4 (2002): 487–96. http://dx.doi.org/10.1162/089120102762671954.

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Анотація:

While automatic text summarization is an area that has received a great deal of attention in recent research, the problem of efficiency in this task has not been frequently addressed. When the size and quantity of documents available on the Internet and from other sources are considered, the need for a highly efficient tool that produces usable summaries is clear. We present a linear-time algorithm for lexical chain computation. The algorithm makes lexical chains a computationally feasible candidate as an intermediate representation for automatic text summarization. A method for evaluating lex

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14

Varade, Saurabh, Ejaaz Sayyed, Vaibhavi Nagtode, and Shilpa Shinde. "Text Summarization using Extractive and Abstractive Methods." ITM Web of Conferences 40 (2021): 03023. http://dx.doi.org/10.1051/itmconf/20214003023.

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Анотація:

Text Summarization is a process where a huge text file is converted into summarized version which will preserve the original meaning and context. The main aim of any text summarization is to provide a accurate and precise summary. One approach is to use a sentence ranking algorithm. This comes under extractive summarization. Here, a graph based ranking algorithm is used to rank the sentences in the text and then top k-scored sentences are included in the summary. The most widely used algorithm to decide the importance of any vertex in a graph based on the information retrieved from the graph i

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15

Mohsin, Muhammad, Shazad Latif, Muhammad Haneef, et al. "Improved Text Summarization of News Articles Using GA-HC and PSO-HC." Applied Sciences 11, no. 22 (2021): 10511. http://dx.doi.org/10.3390/app112210511.

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Анотація:

Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle

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16

Jain, Rekha, Linesh Raja, Sandeep Kumar Sharma, and Devershi Pallavi Bhatt. "Particle swarm optimization model for Hindi text summarization." Journal of Information and Optimization Sciences 45, no. 4 (2024): 839–50. http://dx.doi.org/10.47974/jios-1609.

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Анотація:

Text Summarization is one of the techniques that shorten the original text without vanishing its information as well as meaning. A lot of algorithms exist for text summarization. Two approaches namely Abstractive Text Summarization and Extractive Text Summarization are used for this purpose. In Abstractive text summarization, the entire document is regenerated using a few lines. Whereas in Extractive Text Summarization sentences are filtered based on some ranks assigned to them by a specific algorithm. A lot of work has already been done in languages like English, Chinese etc. In this paper, t

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17

Ke, Xiangyu, Arijit Khan, and Francesco Bonchi. "Multi-relation Graph Summarization." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (2022): 1–30. http://dx.doi.org/10.1145/3494561.

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Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this article, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive mo

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18

Bewoor, M. S., and S. H. Patil. "Empirical Analysis of Single and Multi Document Summarization using Clustering Algorithms." Engineering, Technology & Applied Science Research 8, no. 1 (2018): 2562–67. http://dx.doi.org/10.48084/etasr.1775.

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Анотація:

The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algorithms.

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19

Bewoor, Mrunal S., and Suhas H. Patil. "Empirical Analysis of Single and Multi Document Summarization using Clustering Algorithms." Engineering, Technology & Applied Science Research 8, no. 1 (2018): 2562–67. https://doi.org/10.5281/zenodo.1207394.

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Анотація:

<em>Abstract</em>—The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algo

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20

Amoudi, Ghada, Amal Almansour, and Hanan Saleh Alghamdi. "Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification." Applied Sciences 12, no. 21 (2022): 10980. http://dx.doi.org/10.3390/app122110980.

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Анотація:

The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed meth

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21

Canhasi, Ercan. "Fast document summarization using locality sensitive hashing and memory access efficient node ranking." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 945. http://dx.doi.org/10.11591/ijece.v6i3.9030.

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Анотація:

Text modeling and sentence selection are the fundamental steps of a typical extractive document summarization algorithm. The common text modeling method connects a pair of sentences based on their similarities. Even thought it can effectively represent the sentence similarity graph of given document(s) its big drawback is a large time complexity of $O(n^2)$, where n represents the number of sentences. The quadratic time complexity makes it impractical for large documents. In this paper we propose the fast approximation algorithms for the text modeling and the sentence selection. Our text model

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22

Canhasi, Ercan. "Fast document summarization using locality sensitive hashing and memory access efficient node ranking." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 945. http://dx.doi.org/10.11591/ijece.v6i3.pp945-954.

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Анотація:

Text modeling and sentence selection are the fundamental steps of a typical extractive document summarization algorithm. The common text modeling method connects a pair of sentences based on their similarities. Even thought it can effectively represent the sentence similarity graph of given document(s) its big drawback is a large time complexity of $O(n^2)$, where n represents the number of sentences. The quadratic time complexity makes it impractical for large documents. In this paper we propose the fast approximation algorithms for the text modeling and the sentence selection. Our text model

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23

Flannery, Jeremiah. "Using NLP to Generate MARC Summary Fields for Notre Dame ’s Catholic Pamphlets." International Journal of Librarianship 5, no. 1 (2020): 20–35. http://dx.doi.org/10.23974/ijol.2020.vol5.1.158.

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Анотація:

Three NLP (Natural Language Processing) automated summarization techniques were tested on a special collection of Catholic Pamphlets acquired by Hesburgh Libraries. The automated summaries were generated after feeding the pamphlets as .pdf files into an OCR pipeline. Extensive data cleaning and text preprocessing were necessary before the computer summarization algorithms could be launched. Using the standard ROUGE F1 scoring technique, the Bert Extractive Summarizer technique had the best summarization score. It most closely matched the human reference summaries. The BERT Extractive technique

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24

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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Анотація:

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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25

Челышев, Э. А., М. В. Раскатова, and А. С. Маковец. "Comparative analysis of automatic text quasi-summarization algorithms." Vestnik of Russian New University. Series «Complex systems: models, analysis, management», no. 4 (December 29, 2023): 176–84. http://dx.doi.org/10.18137/rnu.v9187.23.04.p.176.

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Анотація:

Приводится постановка задачи автоматического квазиреферирования текстов, а также подробно рассматриваются такие алгоритмы автоматического квазиреферирования текстов, как алгоритм Луна, латентный семантический анализ, TextRank и LexRank. Выполнена оценка информационной полноты для набора рефератов, сгенерированных при помощи указанных алгоритмов, с использованием метрик информационной близости: метрики, основанной на расстоянии Дженсена – Шеннона, и косинусного сходства, примененных к векторным представлениям исходного текста и полученных рефератов. Проведен статистический анализ полученных рез

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26

Paramanantham, Vinsent, and Dr S. Suresh Kumar. "A Review on Key Features and Novel Methods for Video Summarization." International Journal of Engineering and Advanced Technology 12, no. 3 (2023): 88–105. http://dx.doi.org/10.35940/ijeat.f3737.0212323.

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In this paper, we discuss techniques, algorithms, evaluation methods used in online, offline, supervised, unsupervised, multi-video and clustering methods used for Video Summarization/Multi-view Video Summarization from various references. We have studied different techniques in the literature and described the features used for generating video summaries with evaluation methods, supervised, unsupervised, algorithms and the datasets used. We have covered the survey towards the new frontier of research in computational intelligence technique like ANN (Artificial Neural Network) and other evolut

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27

Vinsent, Paramanantham, and S. Suresh Kumar Dr. "A Review on Key Features and Novel Methods for Video Summarization." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 3 (2023): 88–105. https://doi.org/10.35940/ijeat.F3737.0212323.

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Анотація:

<strong>Abstract: </strong>In this paper, we discuss techniques, algorithms, evaluation methods used in online, offline, supervised, unsupervised, multi-video and clustering methods used for Video Summarization/Multi-view Video Summarization from various references. We have studied different techniques in the literature and described the features used for generating video summaries with evaluation methods, supervised, unsupervised, algorithms and the datasets used.We have covered the survey towards the new frontier of research in computational intelligence technique like ANN (Artificial Neural

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28

Kalyani, BJD, Jaishri Wankhede, and Shaik Shahanaz. "Data Mining Oriented Automatic Scientific Documents Summarization." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 126–30. http://dx.doi.org/10.17762/ijritcc.v11i4.6395.

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Анотація:

The scientific research process usually begins with an examination of the advanced, which may include voluminous publications. Summarizing scientific articles can assist researchers in their research by speeding up the research process. The summary of scientific articles differs from the abstract text in general due to its specific structure and the inclusion of cited sentences. Most of the important information in scientific articles is presented in tables, statistics, and algorithm pseudocode. These features, however, rarely appear in the standard text. Therefore, a number of methods that co

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29

Na, Liu, Tang Di, Lu Ying, Tang Xiao-Jun, and Wang Hai-Wen. "Topic-sensitive multi-document summarization algorithm." Computer Science and Information Systems 12, no. 4 (2015): 1375–89. http://dx.doi.org/10.2298/csis140815060n.

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Анотація:

Latent Dirichlet Allocation (LDA) has been used to generate text corpora topics recently. However, not all the estimated topics are of equal importance or correspond to genuine themes of the domain. Some of the topics can be a collection of irrelevant words or represent insignificant themes. This paper proposed a topic-sensitive algorithm for multi-document summarization. This algorithm uses LDA model and weight linear combination strategy to identify significance topic which is used in sentence weight calculation. Each topic is measured by three different LDA criteria. Significance topic is e

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30

Pandey, Karran, Fanny Chevalier, and Karan Singh. "Juxtaform: interactive visual summarization for exploratory shape design." ACM Transactions on Graphics 42, no. 4 (2023): 1–14. http://dx.doi.org/10.1145/3592436.

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Анотація:

We present juxtaform , a novel approach to the interactive summarization of large shape collections for conceptual shape design. We conduct a formative study to ascertain design goals for creative shape exploration tools. Motivated by a mathematical formulation of these design goals, juxtaform integrates the exploration, analysis, selection, and refinement of large shape collections to support an interactive divergence-convergence shape design workflow. We exploit sparse, segmented sketch-stroke visual abstractions of shape and a novel visual summarization algorithm to balance the needs of sha

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31

Meena, Yogesh Kumar, and Dinesh Gopalani. "Evolutionary Algorithms for Extractive Automatic Text Summarization." Procedia Computer Science 48 (2015): 244–49. http://dx.doi.org/10.1016/j.procs.2015.04.177.

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32

Amini, Amineh, and Teh Ying Wah. "On Density-Based Clustering Algorithms over Evolving Data Streams: A Summarization Paradigm." Applied Mechanics and Materials 263-266 (December 2012): 2234–37. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2234.

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Анотація:

Clustering is one of the prominent classes in the mining data streams. Among various clustering algorithms that have been developed, density-based method has the ability to discover arbitrary shape clusters, and to detect the outliers. Recently, various algorithms adopted density-based methods for clustering data streams. In this paper, we look into three remarkable algorithms in two groups of micro-clustering and grid-based including DenStream, D-Stream, and MR-Stream. We compare the algorithms based on evaluating algorithm performance and clustering quality metrics.

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Wiratmoko, Galih, Husni Thamrin, and Endang Wahyu Pamungkas. "Performance of Machine Learning Algorithms on Automatic Summarization of Indonesian Language Texts." Jurnal Online Informatika 10, no. 1 (2025): 196–204. https://doi.org/10.15575/join.v10i1.1506.

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Анотація:

Automatic text summarization (ATS) has become an essential task for processing huge amounts of information efficiently. ATS has been extensively studied in resource-rich languages like English, but research on summarization for under-resourced languages, such as Bahasa Indonesia, is still limited. Indonesian presents unique linguistic challenges, including its agglutinative structure, borrowed vocabulary, and limited availability of high-quality training data. This study conducts a comparative evaluation of extractive, abstractive, and hybrid models for Indonesian text summarization, utilizing

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Chettah, Khadidja, and Amer Draa. "A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization." International Journal of Natural Computing Research 10, no. 2 (2021): 42–60. http://dx.doi.org/10.4018/ijncr.2021040103.

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Анотація:

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002

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Apostolidis, Evlampios, Eleni Adamantidou, Alexandros Metsai, Vasileios Mezaris, and Ioannis Patras. "Video Summarization Using Deep Neural Networks: A Survey." Proceedings of the IEEE 109, no. 11 (2021): 1838–63. https://doi.org/10.1109/JPROC.2021.3117472.

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Анотація:

Video summarization technologies aim to create a concise and complete synopsis by selecting the most informative parts of the video content. Several approaches have been developed over the last couple of decades, and the current state of the art is represented by methods that rely on modern deep neural network architectures. This work focuses on the recent advances in the area and provides a comprehensive survey of the existing deep-learning-based methods for generic video summarization. After presenting the motivation behind the development of technologies for video summarization, we formulat

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Md, Abdul Quadir, Raghav V. Anand, Senthilkumar Mohan, et al. "Data-Driven Analysis of Privacy Policies Using LexRank and KL Summarizer for Environmental Sustainability." Sustainability 15, no. 7 (2023): 5941. http://dx.doi.org/10.3390/su15075941.

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Анотація:

Natural language processing (NLP) is a field in machine learning that analyses and manipulate huge amounts of data and generates human language. There are a variety of applications of NLP such as sentiment analysis, text summarization, spam filtering, language translation, etc. Since privacy documents are important and legal, they play a vital part in any agreement. These documents are very long, but the important points still have to be read thoroughly. Customers might not have the necessary time or the knowledge to understand all the complexities of a privacy policy document. In this context

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Rautaray, Jyotirmayee, Sangram Panigrahi, and Ajit Kumar Nayak. "Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidirectional long short-term memory classifier for effective single and multi-document summarization." PeerJ Computer Science 10 (December 12, 2024): e2435. https://doi.org/10.7717/peerj-cs.2435.

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Анотація:

The internet now offers a vast amount of information, which makes finding relevant data quite challenging. Text summarization has become a prominent and effective method towards glean important information from numerous documents. Summarization techniques are categorized into single-document and multi-document. Single-document summarization (SDS) targets on single document, whereas multi-document summarization (MDS) combines information from several sources, posing a greater challenge for researchers to create precise summaries. In the realm of automatic text summarization, advanced methods su

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Singco, Van Zachary V., Joel C. Trillo, Cristopher C. Abalorio, James Cloyd M. Bustillo, Junell T. Bojocan, and Michelle C. Elape. "OCR-based Hybrid Image Text Summarizer using Luhn Algorithm with FinetuneTransformer Modelsfor Long Document." International Journal of Emerging Technology and Advanced Engineering 13, no. 2 (2023): 47–56. http://dx.doi.org/10.46338/ijetae0223_07.

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Анотація:

The accessibility of an enormous number of image text documents on the internet has expanded the opportunities to develop a system for image text recognition with text summarization. Several approaches used in ATS in the literature are based on extractive and abstractive techniques; however, few implementations of the hybrid approach were observed. This paper employed state-of-the-art transformer models with the Luhn algorithm for extracted texts using Tesseract OCR. Nine models were generated and tested using the hybrid text summarization approach. Using ROUGE metrics, we compared the propose

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Liu, Qiang, Jiaxing Wei, Hao Liu, and Yimu Ji. "A Hierarchical Parallel Graph Summarization Approach Based on Ranking Nodes." Applied Sciences 13, no. 8 (2023): 4664. http://dx.doi.org/10.3390/app13084664.

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Анотація:

Graph summarization techniques are vital in simplifying and extracting enormous quantities of graph data. Traditional static graph structure-based summarization algorithms generally follow a minimum description length (MDL) style, and concentrate on minimizing the graph storage overhead. However, these methods also suffer from incomprehensive summary dimensions and inefficiency problems. In addition, the need for graph summarization techniques often varies among different graph applications, but an ideal summary method should generally retain the important characteristics of the key nodes in t

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Gahman, Nicholas, and Vinayak Elangovan. "A Comparison of Document Similarity Algorithms." International Journal of Artificial Intelligence & Applications 14, no. 2 (2023): 41–50. http://dx.doi.org/10.5121/ijaia.2023.14204.

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Анотація:

Document similarity is an important part of Natural Language Processing and is most commonly used forplagiarism-detection and text summarization. Thus, finding the overall most effective document similarity algorithm could have a major positive impact on the field of Natural Language Processing. This report setsout to examine the numerous document similarity algorithms, and determine which ones are the mostuseful. It addresses the most effective document similarity algorithm by categorizing them into 3 types ofdocument similarity algorithms: statistical algorithms, neural networks, and corpus/

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Dhammjyoti, Dhawase1 Komal Mohite 2. Harshada Chandane3 Sushakti Bhoir 4. Varsha Mohite5 Prachi Waghmare6. "Document Summarization -A Survey." Scandinavian Journal of Information Systems 35, no. 1 (2023): 124–30. https://doi.org/10.5281/zenodo.7858147.

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Анотація:

The World-Wide Internet has such a large amount of data available. To access this information or to use it from search data engines like Yahoo and Google were created. Because the huge amount of electronic information is growing day by day, the real outcomes have not been  reached. As a result, automatic summarization is in high demand. Automatic summary takes data as input and apply algorithms and different approaches to produces outputs, Summarization saving both time and efforts. Document summarization is the process of compressing a large document into a shorter, more concise version

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Ashwini Mandale-Jadhav. "Text Summarization Using Natural Language Processing." Journal of Electrical Systems 20, no. 11s (2025): 3410–17. https://doi.org/10.52783/jes.8095.

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Анотація:

Text summarization is a crucial task in natural language processing (NLP) that aims to condense large volumes of text into concise and informative summaries. This paper presents a comprehensive study of text summarization techniques using advanced NLP methods. The research focuses on extractive summarization, where key sentences or phrases are extracted from the original text to form a coherent summary. Various approaches such as graph-based algorithms, deep learning models, and hybrid methods combining linguistic features and neural networks are explored and evaluated. The paper also investig

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Vinit, Dhiren Bahua, Chiman Pal Rachit, Uday Sawant Devanshu, Umesh Shetty Aditya, and Arunrao Bakal Shubham. "Text & Video Summarization with Search." Journal of Advanced Research in Artificial Intelligence & It's Applications 1, no. 3 (2024): 12–16. https://doi.org/10.5281/zenodo.11351222.

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Анотація:

<em>Text summarizing is a NLP activity, which reduces massive text volumes into brief summaries. It falls into two categories: abstractive (rephrasing material) and extractive (selecting text parts). Traditional statistical methods and contemporary deep learning techniques are examples of algorithms. While abstractive approaches use Transformer-style sequence-to-sequence models, extractive methods use graph-based algorithms and sentence rating. Keeping context and coherence present challenges. Evaluation criteria that rate summary quality include BLEU and ROUGE. An extension that condenses vid

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Tahseen, Rabia, Uzma Omer, Muhammad Shoaib Farooq, and Faiqa Adnan. "Text Summarization Techniques Using Natural Language Processing: A Systematic Literature Review." VFAST Transactions on Software Engineering 9, no. 4 (2021): 102–8. http://dx.doi.org/10.21015/vtse.v9i4.856.

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Анотація:

In recent years, data has been growing rapidly in almost every domain. Due to this excessiveness of data, there is a need for an automatic text summarizer that summarizes long and numerical data especially textual data without losing its content. Text summarization has been under research for decades and researchers used different summarization methods by using natural language processing and combining various algorithms. This paper presents a systematic literature review by showing a survey of text summarization methods and explains the accuracy of these methods used for text summarization. T

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Al-amri, Redhwan, Raja Kumar Murugesan, Mubarak Almutairi, Kashif Munir, Gamal Alkawsi, and Yahia Baashar. "A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube." Applied Sciences 12, no. 13 (2022): 6523. http://dx.doi.org/10.3390/app12136523.

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Анотація:

As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algor

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D’Silva, Suzanne, Neha Joshi, Sudha Rao, Sangeetha Venkatraman, and Seema Shrawne. "Improved Algorithms for Document Classification &Query-based Multi-Document Summarization." International Journal of Engineering and Technology 3, no. 4 (2011): 404–9. http://dx.doi.org/10.7763/ijet.2011.v3.261.

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Zhang, Chunyan, Junchao Wang, Qinglei Zhou, et al. "A Survey of Automatic Source Code Summarization." Symmetry 14, no. 3 (2022): 471. http://dx.doi.org/10.3390/sym14030471.

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Анотація:

Source code summarization refers to the natural language description of the source code’s function. It can help developers easily understand the semantics of the source code. We can think of the source code and the corresponding summarization as being symmetric. However, the existing source code summarization is mismatched with the source code, missing, or out of date. Manual source code summarization is inefficient and requires a lot of human efforts. To overcome such situations, many studies have been conducted on Automatic Source Code Summarization (ASCS). Given a set of source code, the AS

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Liu, Zixu. "From Extractive to Generative: An Analysis of Automatic Text Summarization Techniques." ITM Web of Conferences 73 (2025): 02008. https://doi.org/10.1051/itmconf/20257302008.

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Анотація:

With the explosive growth of digital content, the demand for effective information retrieval and summarization has become increasingly important. This paper provides a comprehensive review of automated text summarization techniques, focusing on the challenge of condensing large volumes of text into concise summaries. The article explores the evolution of automatic summarization methods, from early extractive techniques to modern generative approaches based on deep learning. The review highlights significant milestones in the development of summarization algorithms, including the emergence of T

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Souri, Adnan, Mohammed Al Achhab, Badr Eddine El Mohajir, Mohamed Naoum, Outman El Hichami, and Abdelali Zbakh. "Arabic Text Summarization Challenges using Deep Learning Techniques: A Review." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 134–42. http://dx.doi.org/10.17762/ijritcc.v11i11s.8079.

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Анотація:

Text summarization is a challenging field in Natural Language Processing due to language modelisation and used techniques to give concise summaries. Dealing with Arabic language does increase the challenge while taking into consideration the many features of the Arabic language, the lack of tools and resources for Arabic, and the Algorithms adaptation and modelisation. In this paper, we present several researches dealing with Arabic Text summarization applying different Algorithms on several Datasets. We then compare all these researches and we give a conclusion to guide researchers on their f

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Praveen Kumar Gupta, Gaurav Dubey, and Akshat Sharma. "Text Summarization Using NLP." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2665–70. https://doi.org/10.47392/irjaeh.2025.0395.

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Анотація:

This paper provides a detailed view of how the development and usage of a natural language processing text summarizer have occurred. Based on its requirement to present condensed yet meaningful conclusions without affecting the original meaning of the given data, this system is built up. Techniques utilized, like feature extraction, preprocessing of the given data, and summarization itself, have also been mentioned within this research paper. It also reviews how well the system performs in benchmark datasets, and it expounds on some of its applications, limitations, and possible further develo

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