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Relevant bibliographies by topics / Text Summarization Tools

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Academic literature on the topic 'Text Summarization Tools'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Text Summarization Tools"

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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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Dr., Imran Khan. "A Comprehensive Study on Text Summarization using Deep Learning." Recent Trends in Data Knowledge Discovery and Data Mining 1, no. 2 (2025): 12–32. https://doi.org/10.5281/zenodo.15582189.

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<em>The internet revolution has resulted in an exponential increase in the volume of digital material available on the web. With the increasing rise of online platforms such as social networking sites, e-commerce websites, blogs, forums, and other digital services, people all over the world are continuously participating in virtual interactions. These platforms have evolved into critical communication, marketing, and collaboration tools, resulting in the everyday collection of massive amounts of textual data. If this data is properly processed, it can provide useful insights that can have a su

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Nikita, Chaudhari, Vora Deepali, Kadam Payal, Khairnar Vaishali, Patil Shruti, and Kotecha Ketan. "Towards efficient knowledge extraction: Natural language processing-based summarization of research paper introductions." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 680–91. https://doi.org/10.11591/ijai.v14.i1.pp680-691.

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Academic and research papers serve as valuable platforms for disseminating expertise and discoveries to diverse audiences. The growing volume of academic papers, with nearly 7 million new publications annually, presents a formidable challenge for students and researchers alike. Consequently, the development of research paper summarization tools has become crucial to distilling crucial insights efficiently. This study examines the effectiveness of pre-trained models like text-to-text transfer transformer (T5), bidirectional encoder representations from transformers (BERT), bidirectional and aut

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Karthik, A. "Text and Video Summarization Using ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2577–81. http://dx.doi.org/10.22214/ijraset.2024.59243.

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Abstract: The Text and Video Summarization tool is a revolutionary approach to manage time and immense amount of data; one must go through. Current text summarization tools struggle to summarize large text files accurately and consistently, especially when dealing with different document formats and file lengths. This paper efficiently handles condenses large amounts of text to meet the growing need for more efficient information consumption accessible to all with a user-friendly interface. By integrating innovative methodologies, advanced NLP techniques, and a diverse dataset enriched with hu

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Liu, Shuhua. "Experiences with and Reflections on Text Summarization Tools." International Journal of Computational Intelligence Systems 2, no. 3 (2009): 202–18. http://dx.doi.org/10.1080/18756891.2009.9727654.

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Yadav, Avaneesh Kumar, Ashish Kumar Maurya, Ranvijay, and Rama Shankar Yadav. "Extractive Text Summarization Using Recent Approaches: A Survey." Ingénierie des systèmes d information 26, no. 1 (2021): 109–21. http://dx.doi.org/10.18280/isi.260112.

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In this era of growing digital media, the volume of text data increases day by day from various sources and may contain entire documents, books, articles, etc. This amount of text is a source of information that may be insignificant, redundant, and sometimes may not carry any meaningful representation. Therefore, we require some techniques and tools that can automatically summarize the enormous amounts of text data and help us to decide whether they are useful or not. Text summarization is a process that generates a brief version of the document in the form of a meaningful summary. It can be c

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Kirmani, Mahira, and Gagandeep Kaur. "Text Summarization for Online and Blended Learning." Scalable Computing: Practice and Experience 25, no. 2 (2024): 972–86. http://dx.doi.org/10.12694/scpe.v25i2.2556.

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Online learning text summarization is vital for managing the constant influx of online information. It involves condensing lengthy online content into concise summaries while retaining the original meaning and information. While several online summarization tools are available, they often fall short in preserving the underlying semantics of the text. In this paper, we introduce an innovative approach to online text summarization that strongly emphasizes capturing and preserving the semantics of the text. Our automatic summarizer leverages distributional semantic models to extract and incorpora

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Losada, David E., and Javier Parapar. "Psychological Features for Automatic Text Summarization." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (2017): 129–49. http://dx.doi.org/10.1142/s0218488517400153.

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Automatically summarizing a document requires conveying the important points of a large document in only a few sentences. Extractive strategies for summarization are based on selecting the most important sentences from the input document(s). We claim here that standard features for estimating sentence importance can be effectively combined with innovative features that encode psychological aspects of communication. We employ Quantitative Text analysis tools for estimating psychological features and we inject them into state-of-the-art extractive summarizers. Our experiments demonstrate that th

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Vipan, Vipan. "A Survey of Text Summarization Systems for Indian Languages." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 12 (2021): 207–14. https://doi.org/10.17762/ijritcc.v9i12.11564.

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Text summarization plays a crucial role in information retrieval, particularly in the context of Indian languages, where linguistic diversity and resource scarcity pose unique challenges. This survey explores the current landscape of text summarization techniques applied to several major Indian languages, including Hindi, Tamil, Marathi, Punjabi, Bengali, and Kannada. The paper provides a comprehensive review of both extractive and abstractive summarization methods, highlighting language-specific strategies, challenges, and progress in the field. It discusses key issues such as the lack of lar

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Dorai, D. Ramya. "AdaptixSummarizer: A Versatile Text Summarization Tool Adaptable to Roles and Styles." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03474.

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Abstract- Text summarization plays a pivotal role in Natural Language Processing (NLP), enabling efficient distillation of key information from extensive and diverse textual content. This paper introduces FlexiSummarizer, a modular, customizable summarization tool designed to accommodate multiple input types—including plain text, web URLs, image files, and PDFs—through an integrated and user-friendly interface. The system combines Optical Character Recognition (OCR) via EasyOCR, PDF parsing through PyMuPDF, and a large language model (LLM) accessed through a backend API currently under develop

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Book chapters on the topic "Text Summarization Tools"

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Basu, Angshuman, Anirban Chatterjee, Rudrakshi Ghosh, et al. "Analysis and Performance of Text Summarization Tools Applied on Indian Languages." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2839-8_28.

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Bathija, Richeeka, Pranav Agarwal, Rakshith Somanna, and G. B. Pallavi. "Multi-document Text Summarization Tool." In Evolutionary Computing and Mobile Sustainable Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5258-8_63.

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Afzal, Mohammad, Supratik Chakraborty, Avriti Chauhan, et al. "VeriAbs : Verification by Abstraction and Test Generation (Competition Contribution)." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45237-7_25.

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Abstract VeriAbs is a strategy selection based reachability verifier for C code. It analyzes the structure of loops, and intervals of inputs to choose one of the four verification strategies implemented in VeriAbs. In this paper, we present VeriAbs version 1.4 with updates in three strategies. We add an array verification technique called full-program induction, and enhance the existing techniques of loop pruning, k-path interval analysis, and disjunctive loop summarization. These changes have improved the verification of programs with arrays, and unstructured loops and unstructured control fl

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Yüksel, Yiğit, and Yalçın Çebi. "TR-SUM: An Automatic Text Summarization Tool for Turkish." In Engineering Cyber-Physical Systems and Critical Infrastructures. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09753-9_21.

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Aruna, Kunti, and Swetha Gatla. "Graph Based Semantically Extractive Tool for Text Summarization Using Similarity Score." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61287-9_10.

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Naseeba, Beebi, U. Sivaji, N. Purushotham, Ramisetti Umamaheswari, Nagendra Panini Challa, and K. Reddy Madhavi. "InsightBot: An Automated Text Summarization and Visualization Tool for Efficient Research Analysis." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88992-9_40.

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Bodrunova, Svetlana S. "Abstractive Summarization of Social Media Texts as a Tool for Representation of Discussion Dynamics: A Scoping Review." In Social Computing and Social Media. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35915-6_4.

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Hassel, Martin, and Hercules Dalianis. "Portable Text Summarization." In Applied Natural Language Processing. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-741-8.ch002.

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Today, with digitally stored information available in abundance, even for many minor languages, this information must by some means be filtered and extracted in order to avoid drowning in it. Automatic summarization is one such technique, where a computer summarizes a longer text to a shorter non-redundant form. The development of advanced summarization systems also for smaller languages may unfortunately prove too costly. Nevertheless, there will still be a need for summarization tools for these languages in order to curb the immense flow of digital information. This chapter sets the focus on

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Aruldoss, Martin, and Miranda Lakshmi Travis. "A Study on Tools and Techniques of Big Data Analytics for Text Summarization From Multi-Documents." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7231-3.ch002.

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Multi-document summarization extracts and summarizes the information without affecting its original context from the different sources of documents. It has been carried out using extractive text summarization and abstractive text summarization. Extractive summarization extracts summaries from verbatim lines, and abstractive summarization extracts new lines of summary from the source documents. Abstractive summarization is an advanced technology compared to extractive summarization. This research studies extractive summarization of multi documents from internet resources using word frequency co

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Bouchard, Guillaume, Stephane Clinchant, and William Darling. "Hot Topic Sensing, Text Analysis, and Summarization." In Advances in Electronic Government, Digital Divide, and Regional Development. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6236-0.ch012.

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Social monitoring platforms are software services that enable the rapid analysis of massive amounts of information (mostly text messages) expressed in social networks. Their usage today mostly focuses on marketing industries that need to reach their customers in a proper way. In the e-government domain, however, such tools have mostly been used by political parties as a support to election campaigns, but few have been actually used to provide a comprehensive understanding of the citizens' needs in everyday life. In this chapter, the authors present a set of data analytics tools that can help p

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Conference papers on the topic "Text Summarization Tools"

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Thi Nguyen, Thuong Hong, Vi Loi Truong, and Ngan Tran Huynh Chau. "Evaluating Text Summarization Tools for Educational Applications: A Study of Copilot, ChatGPT, and Quillbot." In 2025 14th International Conference on Educational and Information Technology (ICEIT). IEEE, 2025. https://doi.org/10.1109/iceit64364.2025.10975903.

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Varun, V., and Steffina Muthukumar. "Two-Step Text Recognition and Summarization of Scanned Documents." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-39473k.

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With the explosion of unstructured textual data circulating the digital space in present times, there has been an increase in the necessity of developing tools that can perform automatic text summarization to allow people to get insights from them easily and extract significant and essential data using Automatic Text Summarizers. The readability of documents can be improved and the time spent on researching for information can be improved by the implementation of text summarization tools. In this project, extractive summarization will be performed on text recognized from scanned documents via

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Lin, Zhuoran, Sifan Chen, Ning Wang, and Hongjun Li. "Evaluating Text Summarization Generated by Popular AI Tools." In The International Seminar on Artificial Intelligence, Networking and Information Technology. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0012283800003807.

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Xu, Hao, Yanan Cao, Ruipeng Jia, Yanbing Liu, and Jianlong Tan. "Sequence Generative Adversarial Network for Long Text Summarization." In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2018. http://dx.doi.org/10.1109/ictai.2018.00045.

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Kia, Mahsa Abazari. "DBERT-ELVA: Discourse-Aware Extractive Text Summarization with Autoencoder." In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2023. http://dx.doi.org/10.1109/ictai59109.2023.00053.

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Zhang, Chi, Lei Zhang, Chong-Jun Wang, and Jun-Yuan Xie. "Text Summarization Based on Sentence Selection with Semantic Representation." In 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2014. http://dx.doi.org/10.1109/ictai.2014.93.

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Pawliczek, Adam, Tereza Žálčík, and Aneta Matula. "From Insight to Impact: How AI Tools Transform Managerial Practice in Czech Companies." In 9th FEB International Scientific Conference. University of Maribor Press, 2025. https://doi.org/10.18690/um.epf.5.2025.59.

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This study investigates the practical applications of artificial intelligence (AI) in managerial work, with a focus on how tools such as large language models (LLMs) support the personal and professional development of managers. It explores how AI is used for decision-making, information analysis, and communication, and assesses its perceived impact on managerial knowledge, skills, and productivity. The research combines a comprehensive literature review with empirical data from a structured survey of 42 managers in Moravian companies, primarily in the Czech Republic. The study evaluates the f

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Galletti, Martina, Michael Anslow, Francesca Bianchi, et al. "Interactive concept-map based summaries for SEND children." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/727.

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Equitable and inclusive quality education is a human right. It is crucial to provide for the learning needs of every child, especially those with learning disabilities. Traditional approaches to learning propose education paths performed with speech therapists. One of the most efficient strategies to help children with reading comprehension difficulties is the creation of a ``concept map'', a structured summary of the written text in a graph structure. Online tools that offer students the possibility to manually create or automatically extract concept maps from text have been created over the

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Litvak, Marina, Natalia Vanetik, Mark Last, and Elena Churkin. "MUSEEC: A Multilingual Text Summarization Tool." In Proceedings of ACL-2016 System Demonstrations. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-4013.

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Ivanova, Malinka, Gabriela Grosseck, and Carmen Holotescu. "ANALYSIS AND MODELING THE DOMAIN OF OPEN EDUCATIONAL RESOURCES FROM LEARNING ANALYTICS PERSPECTIVE." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-178.

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Open Educational Resources (OERs) contributes to ways for knowledge sharing and reusing among given scientific and academic societies. The students and teachers have rapid and unlimited access to content, prepared for different purposes and digital formats. Many projects have been started to extend and improve the existing digital collections. A few analytical tools are developed for automatic identification and monitoring of newly created and added OERs and for tracing the changes in existing OERs. The researchers have applied learning analytics techniques for discovery, analysis and reflecti

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