Log in

Relevant bibliographies by topics / Summarization methods / Journal articles

To see the other types of publications on this topic, follow the link: Summarization methods.

Journal articles on the topic 'Summarization methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Summarization methods.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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,

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

Shaker, Fatima, Mustafa M. Abd Zaid, Ahmed Ali Talib Al-Khazaali, and Zahraa Ali Al-Khzaali. "Text Summarization using different Methods for Deep Learning." BIO Web of Conferences 97 (2024): 00074. http://dx.doi.org/10.1051/bioconf/20249700074.

Full text

Abstract:

In the current era of rapid information expansion, text summarization has become vital for comprehending textual material. Physically condensing large textual volumes is challenging for humans, especially considering the vast amount of text content available online. Text summarization is an active field of research that focuses on summarizing large texts into shorter versions while retaining important information. The writing can be categorized as either extractive or abstractive regarding its summary. Extractive summarizing methods function by determining the significance of individual senten

APA, Harvard, Vancouver, ISO, and other styles

12

Mukhtar, Samal, Claudia Cahya Primadani, Seonah Lee, and Pilsu Jung. "A Comparison of Summarization Methods for Duplicate Software Bug Reports." Electronics 12, no. 16 (2023): 3456. http://dx.doi.org/10.3390/electronics12163456.

Full text

Abstract:

Bug reports vary in length, while some bug reports are lengthy, others are too brief to describe bugs in detail. In such a case, duplicate bug reports can serve as valuable resources for enriching bug descriptions. However, existing bug summarization methods mainly focused on summarizing a single bug report. In this paper, we focus on summarizing duplicate bug reports. By doing so, we aim to obtain an informative summary of bug reports while reducing redundant sentences in the summary. We apply several text summarization methods to duplicate bug reports. We then compare summarization results g

APA, Harvard, Vancouver, ISO, and other styles

13

Kumar, Alok, Utsav Upadhyay, Gajanand Sharma, et al. "Integrating Extractive Techniques and Classification Methods for Legal Document Summarization." International Journal of Data Warehousing and Mining 21, no. 1 (2025): 1–21. https://doi.org/10.4018/ijdwm.379719.

Full text

Abstract:

This article introduces an innovative text summarization mechanism designed to tackle the inherent challenges of condensing lengthy and unstructured legal documents in the context of India. The authors' primary aim is to create a system proficient in extracting crucial information from these documents, producing concise summaries akin to those crafted by humans. The proposed methodology frames summarization as a binary classification problem, employing an extractive summarization technique rooted in statistical features and word vectors. The system strategically identifies summary statements f

APA, Harvard, Vancouver, ISO, and other styles

14

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

15

Majumdar, Dr Jharna, and Spoorthy B. "Comparisons of Video Summarization Methods." IOSR Journal of Computer Engineering 16, no. 5 (2014): 52–56. http://dx.doi.org/10.9790/0661-16525256.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Liu, Yike, Tara Safavi, Abhilash Dighe, and Danai Koutra. "Graph Summarization Methods and Applications." ACM Computing Surveys 51, no. 3 (2018): 1–34. http://dx.doi.org/10.1145/3186727.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

D., K. Kanitha, Muhammad Noorul Mubarak D., and A. Shanavas S. "ISSUES IN MALAYALAM TEXT SUMMARIZATION." International Journal of Applied and Advanced Scientific Research 3, no. 1 (2018): 201–4. https://doi.org/10.5281/zenodo.1205085.

Full text

Abstract:

Text Summarization is the process of creates an abridged version of the original text and it covers overall idea about the document. The human summarization requires lot of time and effort. At the same time summarization system produce summary within a short span of time. It generates summaries or abstracts of large documents. Many techniques have been developed for summarization of text in various languages.  The techniques may be language dependent or independent.  Some techniques may be varies from its discourse structure. The summarization methods can be classified as extractive

APA, Harvard, Vancouver, ISO, and other styles

18

Ahmad Raddi, Muhammad Faris Faisal, Rohayanti Hassan, Noor Hidayah Zakaria, Mohd Zanes Sahid, Nurul Aswa Omar, and Ardian Firosha. "Exploring Current Methods and Trends in Text Summarization: A Systematic Mapping Study." JOIV : International Journal on Informatics Visualization 8, no. 3-2 (2024): 1743. https://doi.org/10.62527/joiv.8.3-2.1654.

Full text

Abstract:

This paper presents a systematic mapping study of the current methods and trends in text summarization, a challenging task in natural language processing that aims to condense information from one or multiple documents into a concise and coherent summary. The paper focuses on applying text summarization for the Malay language, which has received less attention than other languages. The paper employs a three-phased quality assessment procedure to filter and analyze 27 peer-reviewed publications from seven prominent digital libraries, covering 2016 to 2024. The paper addresses two research quest

APA, Harvard, Vancouver, ISO, and other styles

19

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

20

Munot, Nikita, and Sharvari S. Govilkar. "Comparative Study of Text Summarization Methods." International Journal of Computer Applications 102, no. 12 (2014): 33–37. http://dx.doi.org/10.5120/17870-8810.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Hariharan, Shanmugasundaram, and Rengaramanujam Srinivasan. "Enhancements to Graph Based Methods for Single Document Summarization." International Journal of Engineering and Technology 2, no. 1 (2010): 101–11. http://dx.doi.org/10.7763/ijet.2010.v2.107.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

23

Kaswan, Khushwant, and Jyoti Srivastava. "Examining extractive text summarization methods with CNN stories." International Journal of Microsystems and IoT 1, no. 6 (2023): 402–9. https://doi.org/10.5281/zenodo.10399814.

Full text

Abstract:

Because of exponential growth of content in digital format on the Internet, text summarization emerged as an important study area. This study presents an evaluation based on comparison of different extractive summarization techniques, namely Feature-based, Frequency-based, Graph-based and LSA based on the DeepMind CNN Stories dataset. The performance of these techniques was assessed using ROUGE metrics. The findings of our study indicate that the usage of the feature-based approach yielded superior results compared to the other techniques. This was evidenced by the attainment of the highest F1

APA, Harvard, Vancouver, ISO, and other styles

24

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

25

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

<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

APA, Harvard, Vancouver, ISO, and other styles

28

Balaji, J., T. V. Geetha, and Ranjani Parthasarathi. "Abstractive Summarization." International Journal on Semantic Web and Information Systems 12, no. 2 (2016): 76–99. http://dx.doi.org/10.4018/ijswis.2016040104.

Full text

Abstract:

Customization of information from web documents is an immense job that involves mainly the shortening of original texts. This task is carried out using summarization techniques. In general, an automatically generated summary is of two types – extractive and abstractive. Extractive methods use surface level and statistical features for the selection of important sentences, without considering the meaning conveyed by those sentences. In contrast, abstractive methods need a formal semantic representation, where the selection of important components and the rephrasing of the selected components ar

APA, Harvard, Vancouver, ISO, and other styles

29

Omar, Abdulfattah. "Addressing the Problem of Coherence in Automatic Text Summarization: A Latent Semantic Analysis Approach." International Journal of English Linguistics 7, no. 4 (2017): 33. http://dx.doi.org/10.5539/ijel.v7n4p33.

Full text

Abstract:

This article is concerned with addressing the problem of coherence in the automatic summarization of prose fiction texts. Despite the increasing advances within the summarization theory, applications and industry, many problems are still unresolved in relations to the applications of the summarization theory to literature. This can be in part attributed to the peculiar nature of literary texts where standard or typical summarization processes are not amenable for literature. This study, therefore, tends to bridge the gap between literature and summarization theory by proposing a summarization

APA, Harvard, Vancouver, ISO, and other styles

30

Soriyan, Abimbola, and Theresa Omodunbi. "Trends in Multi-document Summarization System Methods." International Journal of Computer Applications 97, no. 16 (2014): 46–52. http://dx.doi.org/10.5120/17095-7804.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Pouriyeh, Seyedamin, Mehdi Allahyari, Qingxia Liu, et al. "Ontology Summarization: Graph-Based Methods and Beyond." International Journal of Semantic Computing 13, no. 02 (2019): 259–83. http://dx.doi.org/10.1142/s1793351x19300012.

Full text

Abstract:

Ontologies have been widely used in numerous and varied applications, e.g. to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper we review existing ontology summarization techniques and focus mainly on graph-based met

APA, Harvard, Vancouver, ISO, and other styles

32

Ermakova, Liana, Jean Valère Cossu, and Josiane Mothe. "A survey on evaluation of summarization methods." Information Processing & Management 56, no. 5 (2019): 1794–814. http://dx.doi.org/10.1016/j.ipm.2019.04.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Shypik, D. V., and Petro I. Bidyuk. "A literature review of abstractive summarization methods." System research and information technologies, no. 4 (December 23, 2019): 66–76. http://dx.doi.org/10.20535/srit.2308-8893.2019.4.07.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Dr., Ashish Kumar Chakraverti, Kumar Pandey Ankit, Dhadse Atul, Kumar Pandey Shivam, and Choudhary Shubham. "A Survey on Methods of Text Summarization." International Journal of Innovative Science and Research Technology 7, no. 4 (2022): 728–34. https://doi.org/10.5281/zenodo.6535661.

Full text

Abstract:

Text summarization has become a reduced form that preserves its data content and general meaning. Thanks to the abundance of data we provide and thanks to the advancement of Internet Technologies, text summarization has become an important tool for deciphering text data. Text abstraction techniques can be divided into removable and invisible abstracts. An abridged summarizing method involves selecting sentences that have a high point in the message according to word structures and sentences and pausing them to produce a summary. The value of sentences is chosen according to the main factual an

APA, Harvard, Vancouver, ISO, and other styles

35

Lucky, Henry, and Derwin Suhartono. "Investigation of Pre-Trained Bidirectional Encoder Representations from Transformers Checkpoints for Indonesian Abstractive Text Summarization." Journal of Information and Communication Technology 21, No.1 (2021): 71–94. http://dx.doi.org/10.32890/jict2022.21.1.4.

Full text

Abstract:

Text summarization aims to reduce text by removing less useful information to obtain information quickly and precisely. In Indonesian abstractive text summarization, the research mostly focuses on multi-document summarization which methods will not work optimally in single-document summarization. As the public summarization datasets and works in English are focusing on single-document summarization, this study emphasized on Indonesian single-document summarization. Abstractive text summarization studies in English frequently use Bidirectional Encoder Representations from Transformers (BERT), a

APA, Harvard, Vancouver, ISO, and other styles

36

Dr. Geetanjali Vinayak Kale. "A Comprehensive Study of Text Summarization with Advent of Large Language Models." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 1073–88. https://doi.org/10.52783/cana.v32.4113.

Full text

Abstract:

Introduction: Communication is at the heart of the human race. With the growth of social media and other communication platforms, the globe is now connected at a single click. People communicate and tend to share information through these platforms. A massive amount of data is being generated and being analysed every second. To tackle the problem of analysing Big Data and withdraw insights from it, is a difficult task. Text summarization is the process of concise representation of textual data so as to extract the most important information out of it. Text summarization plays a major role in a

APA, Harvard, Vancouver, ISO, and other styles

37

Joshi, Chiranjeevi. "Summarization and Translation Using NLP." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 555–58. http://dx.doi.org/10.22214/ijraset.2024.61391.

Full text

Abstract:

Abstract: Text summarization and translation are two critical tasks in natural language processing with significant applications in various domains such as news aggregation, document summarization, machine translation, and information retrieval. In recent years, there has been remarkable progress in the development of techniques and models for both tasks, leveraging advancements in deep learning and neural network architectures. This paper presents a comprehensive review and comparative analysis of state-of-the-art methods in text summarization and translation. First, we provide an overview of

APA, Harvard, Vancouver, ISO, and other styles

38

Zhu, Junnan, Lu Xiang, Yu Zhou, Jiajun Zhang, and Chengqing Zong. "Graph-based Multimodal Ranking Models for Multimodal Summarization." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (2021): 1–21. http://dx.doi.org/10.1145/3445794.

Full text

Abstract:

Multimodal summarization aims to extract the most important information from the multimedia input. It is becoming increasingly popular due to the rapid growth of multimedia data in recent years. There are various researches focusing on different multimodal summarization tasks. However, the existing methods can only generate single-modal output or multimodal output. In addition, most of them need a lot of annotated samples for training, which makes it difficult to be generalized to other tasks or domains. Motivated by this, we propose a unified framework for multimodal summarization that can co

APA, Harvard, Vancouver, ISO, and other styles

39

Kundan Chaudhari, Raj Mahale, Fardeen Khan, Shradha Gaikwad, and Vita Jadhav. "Comprehensive Survey of Abstractive Text Summarization Techniques." International Research Journal on Advanced Engineering and Management (IRJAEM) 6, no. 07 (2024): 2217–31. http://dx.doi.org/10.47392/irjaem.2024.0323.

Full text

Abstract:

Text summarization using pre-trained encoders has become a crucial technique for efficiently managing large volumes of text data. The rise of automatic summarization systems addresses the need to process ever-increasing data while meeting user-specific requirements. Recent scientific research highlights significant advancements in abstractive summarization, with a particular focus on neural network-based methods. A detailed review of various neural network models for abstractive summarization identifies five key components essential to their design: encoder-decoder architecture, mechanisms, tr

APA, Harvard, Vancouver, ISO, and other styles

40

ÖZKAN, Adem. "Özetleme Tekniğinin Dil Öğretiminde Kullanımı Üzerine Kapsamli Bir İnceleme ve PQRST Tekniği." International Journal of Social Sciences 9, no. 38 (2025): 188–223. https://doi.org/10.52096/usbd.9.38.11.

Full text

Abstract:

This study examines the importance of teaching summarization techniques and different application strategies. Summarization is a significant method that enables students to comprehend texts and enhance their written communication skills. The research focuses on how summarization strategies can be effectively taught to students and implemented in instructional processes. Techniques such as generalization, deletion, and restructuring are used to help students make texts shorter, concise, and understandable. Additionally, it emphasizes that summarization techniques can enhance not only students'

APA, Harvard, Vancouver, ISO, and other styles

41

Parimoo, Rohit, Rohit Sharma, Naleen Gaur, Nimish Jain, and Sweeta Bansal. "Applying Text Rank to Build an Automatic Text Summarization Web Application." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 865–67. http://dx.doi.org/10.22214/ijraset.2022.40766.

Full text

Abstract:

Abstract: Automatic Text Summarization is one of the most trending research areas in the field of Natural Language Processing. The main aim of text summarization is to reduce the size of a text without losing any important information. Various techniques can be used for automatic summarization of text. In this paper we are going to focus on the automatic summarization of text using graph-based methods. In particular, we are going to discuss the implementation of a general-purpose web application which performs automatic summarization on the text entered using the Text Rank Algorithm. Summariza

APA, Harvard, Vancouver, ISO, and other styles

42

KUZNIETSOV, Oleksii, and Gennadiy KYSELOV. "USING AND ANALYSIS OF FORMAL METHODS FOR EVALUATING THE RELEVANCE OF AUTOMATICALLY GENERATED SUMMARIES OF INFORMATIONAL TEXTS." Advanced Information Technology, no. 1 (3) (2024): 32–48. https://doi.org/10.17721/ait.2024.1.04.

Full text

Abstract:

B a c k g r o u n d . The article reviews existing approaches to evaluating the quality of automatically generated summaries of informational texts. It provides an overview of automatic summarization methods, including classical approaches and modern models based on artificial intelligence. The review covers extractive summarization methods such as TF-IDF and PageRank, as well as graph-based methods, specifically TextRank. Special attention is given to abstractive approaches, including Generative Pretrained Transformer (GPT) and Bidirectional and Auto-Regressive Transformers (BART) models. The

APA, Harvard, Vancouver, ISO, and other styles

43

Yoon, Ui-Nyoung, Myung-Duk Hong, and Geun-Sik Jo. "Interp-SUM: Unsupervised Video Summarization with Piecewise Linear Interpolation." Sensors 21, no. 13 (2021): 4562. http://dx.doi.org/10.3390/s21134562.

Full text

Abstract:

This paper addresses the problem of unsupervised video summarization. Video summarization helps people browse large-scale videos easily with a summary from the selected frames of the video. In this paper, we propose an unsupervised video summarization method with piecewise linear interpolation (Interp-SUM). Our method aims to improve summarization performance and generate a natural sequence of keyframes with predicting importance scores of each frame utilizing the interpolation method. To train the video summarization network, we exploit a reinforcement learning-based framework with an explici

APA, Harvard, Vancouver, ISO, and other styles

44

Mankar, Prafull S., and Avinash B. Manwar. "An Exploration of Extractive method and Abstractive Method of Text Summarization with Various Approaches, Techniques and Datasets." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 422–30. http://dx.doi.org/10.22214/ijraset.2024.65100.

Full text

Abstract:

Abstract: In today’s era, an enormous amount of data available which is in complex form such as Social media content, Images, Audio, Video and Text data form. The mechanism is very much needed to provide these types of data in simple and easy to understandable form. In this paper, importance is given on text summarization of large amount of textual data of documents must be specified in concise and understandable form. Automatic Text Summarization is the technique which is used to represent most significant concepts in precise and comprehensible form from original or source document to target

APA, Harvard, Vancouver, ISO, and other styles

45

Khotimah, Nurul, Adi Wibowo P, Bryan Andreas, and Abba Suganda Girsang. "A Review Paper on Automatic Text Summarization in Indonesia Language." International Journal of Emerging Technology and Advanced Engineering 11, no. 8 (2021): 89–96. http://dx.doi.org/10.46338/ijetae0821_11.

Full text

Abstract:

Text summarization is one problem in natural language processing that generates a brief version of the original document. This research took attention for some researchers in this last decade and growing fast, including Indonesia language. This paper aims to recap summarization text research especially in Indonesia language. As usual, this paper discusses two summarization approaches, extractive and abstractive. In fact, the number of research of extractive is more than abstractive. This paper investigates some methods such as Statistical Based Approach, Graph Based Approach, Machine Learning

APA, Harvard, Vancouver, ISO, and other styles

46

张, 轩. "Summarization of Image Segmentation Methods for Adhesion Particles." Journal of Image and Signal Processing 07, no. 03 (2018): 113–18. http://dx.doi.org/10.12677/jisp.2018.73013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Can, Çağdaş, Özgün Koşaner, and Özlem Aktaş. "A Survey to Text Summarization Methods for Turkish." International Journal of Computer Applications 144, no. 6 (2016): 23–28. http://dx.doi.org/10.5120/ijca2016910358.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Riahi Samani, Zahra, and Mohsen Ebrahimi Moghaddam. "Image Collection Summarization Method Based on Semantic Hierarchies." AI 1, no. 2 (2020): 209–28. http://dx.doi.org/10.3390/ai1020014.

Full text

Abstract:

The size of internet image collections is increasing drastically. As a result, new techniques are required to facilitate users in browsing, navigation, and summarization of these large volume collections. Image collection summarization methods present users with a set of exemplar images as the most representative ones from the initial image collection. In this study, an image collection summarization technique was introduced according to semantic hierarchies among them. In the proposed approach, images were mapped to the nodes of a pre-defined domain ontology. In this way, a semantic hierarchi

APA, Harvard, Vancouver, ISO, and other styles

49

T V, Shruthi, Rahul R, and M. Neha. "AESTHETIC DRIVEN VIDEO SUMMARIZATION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem40002.

Full text

Abstract:

Video summarization condenses long, monotonous videos into concise representations, retaining critical events, objects, and sequences while minimizing redundancy. This allows users to quickly extract insights without watching the entire footage, benefiting fields like entertainment, education, surveillance, and recommendation systems. By integrating object detection a deep learning technique for identifying and localizing objects into the summarization process, the approach enhances relevance and context- awareness. This fusion ensures more accurate and meaningful summaries compared to traditi

APA, Harvard, Vancouver, ISO, and other styles

50

TATAR, DOINA, ANDREEA MIHIS, DANA LUPSA, and EMMA TAMAIANU-MORITA. "ENTAILMENT-BASED LINEAR SEGMENTATION IN SUMMARIZATION." International Journal of Software Engineering and Knowledge Engineering 19, no. 08 (2009): 1023–38. http://dx.doi.org/10.1142/s0218194009004520.

Full text

Abstract:

This paper presents some original methods for text summarization of a single source document by extraction. The methods are based on some of our own text segmentation algorithms. We denote them as logical segmentation because for all these methods (LTT, ArcInt and ArcReal) the score of a sentence is calculated starting from the number of sentences which are entailed by it. For a text (which is a sequence of sentences) the scores form a structure which indicates how the most important sentences alternate with less important ones and organizes the text according to its logical content. The secon

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!