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Journal articles on the topic 'Summarization'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Larson, Martha. "Automatic Summarization." Foundations and Trends® in Information Retrieval 5, no. 3 (2012): 235–422. http://dx.doi.org/10.1561/1500000020.

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D, Manju, Radhamani V, Dhanush Kannan A, Kavya B, Sangavi S, and Swetha Srinivasan. "TEXT SUMMARIZATION." YMER Digital 21, no. 07 (2022): 173–82. http://dx.doi.org/10.37896/ymer21.07/13.

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n the last few years, a huge amount of text data from different sources has been created every day. The enormous data which needs to be processed contains valuable detail which needs to be efficiently summarized so that it serves a purpose. It is very tedious to summarize and classify large amounts of documents when done manually. It becomes cumbersome to develop a summary taking every semantics into consideration. Therefore, automatic text summarization acts as a solution. Text summarization can help in understanding the huge corpus by providing a gist of the corpus enabling comprehension in

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Maña-López, Manuel J., Manuel De Buenaga, and José M. Gómez-Hidalgo. "Multidocument summarization." ACM Transactions on Information Systems 22, no. 2 (2004): 215–41. http://dx.doi.org/10.1145/984321.984323.

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Vikas, A., Pradyumna G.V.N, and Tahir Ahmed Shaik. "Text Summarization." International Journal of Engineering and Computer Science 9, no. 2 (2020): 24940–45. http://dx.doi.org/10.18535/ijecs/v9i2.4437.

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In this new era, where tremendous information is available on the internet, it is most important to provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of a large documents of text. There are plenty of text material available on the internet. So, there is a problem of searching for relevant documents from the number of documents available and absorbing relevant information from it. In order to solve the above two problems, the automatic text summarization is very much necessary. Text summa

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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.

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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

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Indu Nair, Dr V. "YouTube Summarization." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46776.

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Abstract The YouTube Summarizer is an AI-powered web application designed to enhance digital content consumption by providing concise summaries of YouTube videos. Developed using the Next.js framework, the platform integrates state-of-the-art language models such as GPT, Gemini, and LLaMA to generate context-aware summaries from extracted video transcripts. It supports multilingual outputs and offers summary customization—like video-style or podcast-style formats—tailored to user preferences. The application features a sleek, responsive UI with session history tracking, making it accessible an

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Jha, Nitesh Kumar, and Arnab Mitra. "Introducing Word's Importance Level-Based Text Summarization Using Tree Structure." International Journal of Information Retrieval Research 10, no. 1 (2020): 13–33. http://dx.doi.org/10.4018/ijirr.2020010102.

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Text-summarization plays a significant role towards quick knowledge acquisition from any text-based knowledge resource. To enhance the text-summarization process, a new approach towards automatic text-summarization is presented in this article that facilitates level (word importance factor)-based automated text-summarization. An equivalent tree is produced from the directed-graph during the input text processing with WordNet. Detailed investigations further ensure that the execution time for proposed automatic text-summarization, is strictly following a linear relationship with reference to th

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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.

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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

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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.

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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

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Hua, Hang, Yunlong Tang, Chenliang Xu, and Jiebo Luo. "V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 3599–607. https://doi.org/10.1609/aaai.v39i4.32374.

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Video summarization aims to create short, accurate, and cohesive summaries of longer videos. Despite the existence of various video summarization datasets, a notable limitation is their limited amount of source videos, which hampers the effective training of advanced large vision-language models (VLMs). Additionally, most existing datasets are created for video-to-video summarization, overlooking the contemporary need for multimodal video content summarization. Recent efforts have been made to expand from unimodal to multimodal video summarization, categorizing the task into three sub-tasks ba

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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.

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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

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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.

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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'

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Peronikolis, Michail, and Costas Panagiotakis. "Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets." Applied Sciences 14, no. 11 (2024): 4400. http://dx.doi.org/10.3390/app14114400.

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In recent years, the scientific and technological developments have led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context,

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Diedrichsen, Elke. "Linguistic challenges in automatic summarization technology." Journal of Computer-Assisted Linguistic Research 1, no. 1 (2017): 40. http://dx.doi.org/10.4995/jclr.2017.7787.

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Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse

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Howlader, Prottyee, Prapti Paul, Meghana Madavi, Laxmi Bewoor, and V. S. Deshpande. "Fine Tuning Transformer Based BERT Model for Generating the Automatic Book Summary." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (2022): 347–52. http://dx.doi.org/10.17762/ijritcc.v10i1s.5902.

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Major text summarization research is mainly focusing on summarizing short documents and very few works is witnessed for long document summarization. Additionally, extractive summarization is more addressed as compared with abstractive summarization. Abstractive summarization, unlike extractive summarization, does not only copy essential words from the original text but requires paraphrasing to get close to human generated summary. The machine learning, deep learning models are adapted to contemporary pre-trained models like transformers. Transformer based Language models gaining a lot of atten

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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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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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R., Priyadharshini, and Marikkannan M. "A Study Analysis based on Text Summarization Methods." Journal of Advancement in Parallel Computing 4, no. 1 (2021): 1–6. https://doi.org/10.5281/zenodo.4802978.

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Everybody wants the simple and easy to use things in this busy world and then all are expected the ready to serve things in anything. So that same thing also discovered day by day in the computer era, In the computer field the concept of Text summarization creates the most attention. Text summarization is a crucial and timely tool to reduce the text data. This paper produces the main idea of the text summarization, like what is text summarization and also describes what techniques are used in this field currently. Text summarization reduces the huge amount of data into simple text document, it

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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.

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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

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Thomas, Sinnu Susan, Sumana Gupta, and Venkatesh K. Subramanian. "Perceptual Video Summarization—A New Framework for Video Summarization." IEEE Transactions on Circuits and Systems for Video Technology 27, no. 8 (2017): 1790–802. http://dx.doi.org/10.1109/tcsvt.2016.2556558.

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Rahamat Basha, S., J. Keziya Rani, and J. J. C. Prasad Yadav. "A Novel Summarization-based Approach for Feature Reduction Enhancing Text Classification Accuracy." Engineering, Technology & Applied Science Research 9, no. 6 (2019): 5001–5. http://dx.doi.org/10.48084/etasr.3173.

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Automatic summarization is the process of shortening one (in single document summarization) or multiple documents (in multi-document summarization). In this paper, a new feature selection method for the nearest neighbor classifier by summarizing the original training documents based on sentence importance measure is proposed. Our approach for single document summarization uses two measures for sentence similarity: the frequency of the terms in one sentence and the similarity of that sentence to other sentences. All sentences were ranked accordingly and the sentences with top ranks (with a thre

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Rahamat, Basha S., Rani J. Keziya, and Yadav J. J. C. Prasad. "A Novel Summarization-based Approach for Feature Reduction Enhancing Text Classification Accuracy." Engineering, Technology & Applied Science Research 9, no. 6 (2019): 5001–5. https://doi.org/10.5281/zenodo.3566535.

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Automatic summarization is the process of shortening one (in single document summarization) or multiple documents (in multi-document summarization). In this paper, a new feature selection method for the nearest neighbor classifier by summarizing the original training documents based on sentence importance measure is proposed. Our approach for single document summarization uses two measures for sentence similarity: the frequency of the terms in one sentence and the similarity of that sentence to other sentences. All sentences were ranked accordingly and the sentences with top ranks (with a thre

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Karunamurthy, Dr A., R. Ramakrishnan, J. Nivetha, and S. Varsha. "Auto Synopsis: An Intelligent Web-Based Application for Automating Content Summarization Using Advanced NLP Techniques." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–6. https://doi.org/10.55041/isjem02157.

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Auto Synopsis introduces an efficient web-based application designed to automate text summarization using advanced natural language processing (NLP) techniques. Built with Flask, the system extracts and processes textual content, transforming it into concise, meaningful summaries. The text undergoes preprocessing steps, including tokenization, lemmatization, and stemming, to prepare it for analysis. Auto Synopsis supports both extractive and abstractive summarization. Extractive summarization selects and extracts important sentences or segments from the original text, while abstractive summari

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Kopeć, Mateusz. "Three-step coreference-based summarizer for Polish news texts." Poznan Studies in Contemporary Linguistics 55, no. 2 (2019): 397–443. http://dx.doi.org/10.1515/psicl-2019-0015.

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Abstract This article addresses the problem of automatic summarization of press articles in Polish. The main novelty of this research lays in the proposal of a three-step summarization algorithm which benefits from using coreference information. In related work section, all coreference-based approaches to summarization are presented. Then we describe in detail all publicly available summarization tools developed for Polish language. We state the problem of single-document press article summarization for Polish, describing the training and evaluation dataset: the POLISH SUMMARIES CORPUS. Next,

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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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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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ber, Bam, and Micah Jason. "News Filtering and Summarization System Architecture for Recognition and Summarization of News Pages." Bonfring International Journal of Data Mining 7, no. 2 (2017): 11–15. http://dx.doi.org/10.9756/bijdm.8339.

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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.

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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

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Pivovarov, Rimma, and Noémie Elhadad. "Automated methods for the summarization of electronic health records." Journal of the American Medical Informatics Association 22, no. 5 (2015): 938–47. http://dx.doi.org/10.1093/jamia/ocv032.

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Abstract Objectives This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation. Target audience The target audience for this review includes researchers, designers, and informaticians who are concerned about the problem of information overload in the clinical setting as well as both users and developers of clinical summarization systems. Scope Automated summarization has been a lon

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Ahuir, Vicent, José-Ángel González, Lluís-F. Hurtado, and Encarna Segarra. "Abstractive Summarizers Become Emotional on News Summarization." Applied Sciences 14, no. 2 (2024): 713. http://dx.doi.org/10.3390/app14020713.

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Emotions are central to understanding contemporary journalism; however, they are overlooked in automatic news summarization. Actually, summaries are an entry point to the source article that could favor some emotions to captivate the reader. Nevertheless, the emotional content of summarization corpora and the emotional behavior of summarization models are still unexplored. In this work, we explore the usage of established methodologies to study the emotional content of summarization corpora and the emotional behavior of summarization models. Using these methodologies, we study the emotional co

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Sanchez-Gomez, Jesús Manuel, Miguel Ángel Vega-Rodríguez, and Sánchez Carlos Javier Pérez. "Automatic update summarization by a multi-objective number-one-selection genetic approach." IEEE Transactions on Cybernetics 53, no. 12 (2023): 7443–54. https://doi.org/10.1109/TCYB.2022.3223163.

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Currently, the explosive growth of the information available on the internet makes automatic text summarization systems increasingly important. A particularly relevant challenge is the update summarization task. Update summarization differs from traditional summarization in its dynamic nature. While traditional summarization is static, i.e., the document collections about a specific topic remain unchanged, update summarization addresses dynamic document collections based on a specific topic. Therefore, update summarization consists of summarizing the new document collection under the assumptio

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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.

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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

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Zhang, Mengli, Gang Zhou, Wanting Yu, Ningbo Huang, and Wenfen Liu. "A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (August 1, 2022): 1–21. http://dx.doi.org/10.1155/2022/7132226.

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With the rapid development of the Internet, the massive amount of web textual data has grown exponentially, which has brought considerable challenges to downstream tasks, such as document management, text classification, and information retrieval. Automatic text summarization (ATS) is becoming an extremely important means to solve this problem. The core of ATS is to mine the gist of the original text and automatically generate a concise and readable summary. Recently, to better balance and develop these two aspects, deep learning (DL)-based abstractive summarization models have been developed.

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Kartamanah, Fatih Fauzan, Aldy Rialdy Atmadja, and Ichsan Budiman. "Analyzing PEGASUS Model Performance with ROUGE on Indonesian News Summarization." sinkron 9, no. 1 (2025): 31–42. https://doi.org/10.33395/sinkron.v9i1.14303.

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Text summarization technology has been rapidly advancing, playing a vital role in improving information accessibility and reducing reading time within Natural Language Processing (NLP) research. There are two primary approaches to text summarization: extractive and abstractive. Extractive methods focus on selecting key sentences or phrases directly from the source text, while abstractive summarization generates new sentences that capture the essence of the content. Abstractive summarization, although more flexible, poses greater challenges in maintaining coherence and contextual relevance due

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D., K. Kanitha, Muhammad Noorul Mubarak D., and A. Shanavas S. "COMPARISON OF TEXT SUMMARIZER IN INDIAN LANGUAGES." International Journal of Advanced Trends in Engineering and Technology 3, no. 1 (2018): 79–82. https://doi.org/10.5281/zenodo.1205087.

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Text summarization is the process of extracting the relevant information from a source text keeps the significant information. Mainly two types of text summarization methods such as abstractive and extractive. The extractive summarization ranks all sentences and high scored sentences are selected as summary. The abstractive summarization understands the content of a document and re-state in few words. This paper discusses about various text summarization methods followed by the Indian languages. The existing algorithms are explained and then the merits and demerits are discussed. This paper al

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Bai, Jiyang, and Peixiang Zhao. "Poligras: Policy-Based Graph Summarization." Proceedings of the VLDB Endowment 17, no. 10 (2024): 2432–44. http://dx.doi.org/10.14778/3675034.3675037.

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Large graphs are ubiquitous. Their sizes, rates of growth, and complexity, however, have significantly outpaced human capabilities to ingest and make sense of them. As a cost-effective graph simplification technique, graph summarization is aimed to reduce large graphs into concise, structure-preserving, and quality-enhanced summaries readily available for efficient graph storage, processing, and visualization. Concretely, given a graph G , graph summarization condenses G into a succinct representation comprising (1) a supergraph with supernodes representing disjoint sets of vertices of G and s

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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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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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Zhang, Xinyuan, Ruiyi Zhang, Manzil Zaheer, and Amr Ahmed. "Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14489–97. http://dx.doi.org/10.1609/aaai.v35i16.17703.

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High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utteran

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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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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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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.

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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

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Thanh, Tam Doan, Tan Minh Nguyen, Thai Binh Nguyen, et al. "Graph-based and generative approaches to multi-document summarization." Journal of Computer Science and Cybernetics 40, no. 3 (2024): 203–17. https://doi.org/10.15625/1813-9663/18353.

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Multi-document summarization is a challenging problem in the Natural Language Processing field that has drawn a lot of interest from the research community. In this paper, we propose a two-phase pipeline to tackle the Vietnamese abstractive multi-document summarization task. The initial phase of the pipeline involves an extractive summarization stage including two different systems. The first system employs a hybrid model based on the TextRank algorithm and a text correlation consideration mechanism. The second system is a modified version of SummPip - an unsupervised graph-based method for mu

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Timalsina, Bipin, Nawaraj Paudel, and Tej Bahadur Shahi. "Attention based Recurrent Neural Network for Nepali Text Summarization." Journal of Institute of Science and Technology 27, no. 1 (2022): 141–48. http://dx.doi.org/10.3126/jist.v27i1.46709.

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Automatic text summarization has been a challenging topic in natural language processing (NLP) as it demands preserving important information while summarizing the large text into a summary. Extractive and abstractive text summarization are widely investigated approaches for text summarization. In extractive summarization, the important sentence from the large text is extracted and combined to create a summary whereas abstractive summarization creates a summary that is more focused on meaning, rather than content. Therefore, abstractive summarization gained more attention from researchers in t

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