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Journal articles on the topic 'Text Summarization Tools'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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Narasani, Snehitha. "Synoptix Summarizer: A Role and Style Adaptive Text Summarization Tool with Multi-Input Support." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47724.

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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 Synoptix Summarizer, 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 dev

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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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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.

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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

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Verma, Pradeepika, and Anshul Verma. "Accountability of NLP Tools in Text Summarization for Indian Languages." Journal of scientific research 64, no. 01 (2020): 258–63. http://dx.doi.org/10.37398/jsr.2020.640149.

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

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

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Kavitha Soppari, Nuthana Basupally, Harika Toomu, and Pavan Kalyan Bijili. "Offline LLM: Generating human like responses without internet." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1823–27. https://doi.org/10.30574/wjarr.2025.26.2.1783.

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This study explores the integration of lightweight and offline-capable natural language processing (NLP) tools for extractive and abstractive text summarization in resource-constrained environments. Drawing from foundational work such as TextRank (Mihalcea & Tarau, 2004) and the NLTK toolkit (Bird et al., 2009), the system combines graph-based extractive summarization and frequency-based keyword extraction for efficient offline text analysis. PyMuPDF facilitates accurate PDF text extraction, enabling document conversion into analyzable formats. Abstractive summarization leverages the T5-sm

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Vo, Tham. "SE4ExSum: An Integrated Semantic-aware Neural Approach with Graph Convolutional Network for Extractive Text Summarization." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (2021): 1–22. http://dx.doi.org/10.1145/3464426.

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Recently, advanced techniques in deep learning such as recurrent neural network (GRU, LSTM and Bi-LSTM) and auto-encoding (attention-based transformer and BERT) have achieved great successes in multiple application domains including text summarization. Recent state-of-the-art encoding-based text summarization models such as BertSum, PreSum and DiscoBert have demonstrated significant improvements on extractive text summarization tasks. However, recent models still encounter common problems related to the language-specific dependency which requires the supports of the external NLP tools. Besides

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Pawar, Shaila. "BRIEFLY Video Transcript Summarizer." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem31136.

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In an age where video content proliferates across digital platforms, the need for efficient video transcript summarization tools has become increasingly crucial. This abstract introduces a novel Video Transcript Summarizer designed to automatically generate concise and informative summaries from lengthy video transcripts. By utilizing techniques such as text summarization, keyword extraction, and sentiment analysis, the summarizer seeks to capture the essence of the video while preserving its context and relevance. The abstract concludes by highlighting the potential applications of such a too

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Chaudhari, Nikita, Deepali Vora, Payal Kadam, Vaishali Khairnar, Shruti Patil, and Ketan Kotecha. "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. http://dx.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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Kondath, Manju, David Peter Suseelan, and Sumam Mary Idicula. "Extractive summarization of Malayalam documents using latent Dirichlet allocation: An experience." Journal of Intelligent Systems 31, no. 1 (2022): 393–406. http://dx.doi.org/10.1515/jisys-2022-0027.

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Abstract Automatic text summarization (ATS) extracts information from a source text and presents it to the user in a condensed form while preserving its primary content. Many text summarization approaches have been investigated in the literature for highly resourced languages. At the same time, ATS is a complicated and challenging task for under-resourced languages like Malayalam. The lack of a standard corpus and enough processing tools are challenges when it comes to language processing. In the absence of a standard corpus, we have developed a dataset consisting of Malayalam news articles. T

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Mohammed, Hashim Younis, and M. I. Zebari Ibrahim. "Enhancing Medical Text Summarization using Transformer-Based NLP Models for Clinical Decision Support." Engineering and Technology Journal 10, no. 05 (2025): 5264–73. https://doi.org/10.5281/zenodo.15550842.

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Medical text summarization plays a crucial role in clinical decision support by enabling healthcare professionals to quickly access essential information from vast amounts of unstructured medical texts. With the rapid advancements in Natural Language Processing (NLP), transformer-based models have emerged as powerful tools for generating high-quality summaries. This paper investigates the effectiveness of state-of-the-art transformer models, such as BERT, GPT, and T5, in summarizing medical texts while preserving critical information. We conduct comprehensive evaluations using benchmark datase

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CHALI, YLLIAS. "TEXT SUMMARIZATION USING LEXICAL COHESION: APPROACHES AND EVALUATIONS." International Journal on Artificial Intelligence Tools 17, no. 02 (2008): 259–78. http://dx.doi.org/10.1142/s0218213008003881.

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Popularity of the Internet has contributed towards the explosive growth of online information, and it is especially useful to have tools which can help users digest information content. Text summarization addresses this need by taking a source text, selecting the most important portions of it, and presenting coherent summary to the user in a manner sensitive to the user's or application's needs. The goal of this paper is to show how these objectives can be achieved through an efficient use of lexical cohesion. The current work addresses both generic and query-based summaries in the context of

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Sorokina, Svetlana. "Intelligent Text Processing: A Review of Automated Summarization Methods." Virtual Communication and Social Networks 3, no. 3 (2024): 203–22. http://dx.doi.org/10.21603/2782-4799-2024-3-3-203-222.

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Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, developed based on interdisciplinary research in linguistics, computer technologies, and artificial intelligence. It focuses on syntactic and lexical techniques employed by neural network models for text compression. The paper presents performance examples of such AI-powered services as QuillBot, Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI Chat

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D’Silva, Jovi, and Uzzal Sharma. "Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1990. http://dx.doi.org/10.11591/ijece.v12i2.pp1990-2000.

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<span lang="EN-US">Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText &l

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Jovi, D'Silva, and Sharma Uzzal. "Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1990–2000. https://doi.org/10.11591/ijece.v12i2.pp1990-2000.

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Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText pre-trained word embe

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Alwesabi, Khaled, and Weihua Gui. "A Novel Method Based on Hybrid Summarization System for Arabic Documents." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 835–46. http://dx.doi.org/10.1166/jctn.2017.6070.

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With continuing global growth of Internet networks and the increase of the amount of texts comes the increased need for automatic summarization. Researchers introduced several methods for article summarization and highlighted the most important information pertaining to this subject. They also discussed the linguistic methods that rely on rhetorical and linguistic tools to analyze the text by pinpointing the semantic and rhetorical links between different text units to determine the proportional relationship between each other, while others discuss the statistical methods using a semi-supervis

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Vasili, Roland, Endri Xhina, Ilia Ninka, and Thomas Souliotis. "A STUDY OF SUMMARIZATION TECHNIQUES IN ALBANIAN LANGUAGE." Knowledge International Journal 28, no. 7 (2018): 2251–57. http://dx.doi.org/10.35120/kij28072251r.

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In recent years, technology has developed a lot and has revolutionized our perspective of the world. Technology and more precisely digital technology has created amazing tools, giving immediate access to anyone interested to any information he may need. This digital revolution of all media like computers, smartphones, etc. has produced a huge amount of digital data to be handled. In our research we care about one aspect of this data, the text data, and the way we can efficiently handle text and produce meaningful summaries. Thus, it is only until recently that text mining has become an interes

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Hosseini, Khah Tayebe, Abbas Ahmadi, and Azadeh Mohebi. "Improving Automatic Text Summarization of Persian Texts Using Natural Language Processing Methods and Similarity Graphs." Journal of Information Processing and Management 33, no. 2 (2017): 885–914. https://doi.org/10.5281/zenodo.14007803.

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A significant portion of accessible information is stored in text databases, which include a large collection of various documents and resources (such as news articles, books, emails, and web pages). The substantial increase in this type of information highlights the need for tools to automatically evaluate textual resources more than ever. Among these, automatic text summarization is one of the solutions that reduces users' time wastage. Extractive summarization involves selecting the most important sentences from a text to shorten it while retaining the essential information of the input tex

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Viegas, Franklin Robert. "Enhancing Study Experience using Handwritten Character and Digit Recognition and Text Summarization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30665.

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The integration of Handwritten characters and, Digit Recognition and Deep Learning in education heralds a transformative era in learning methodologies. This abstract delves into the multifaceted benefits derived from the amalgamation of these technologies, redefining the educational landscape. Handwritten characters and Digit Recognition technology facilitates the seamless digitization of handwritten content, transcending the limitations of manual note-taking. Its introduction into educational frameworks enhances accessibility, promotes organization, and augments the searchability of diverse e

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Veena R. "Topic Driven Text Extraction for Kannada Document Summarization Using LDA." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 138–48. https://doi.org/10.52783/jisem.v10i36s.6324.

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Automatic Text Summarization (ATS) compacts source content into a concise format while preserving core information. While extensively studied for resource-rich languages, ATS remains challenging for low-resource languages like Kannada due to limited corpora and NLP tools. This work introduces an extractive, topic-driven method for summarizing Kannada news articles from multiple documents. We developed a custom dataset of 100 Kannada news story sets (3 articles per set) to address the lack of standardized benchmarks. The proposed approach leverages Latent Dirichlet Allocation (LDA) to identify

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Aswana, T. P., and P. R. Resija. "Abstractive Book Summarization Using Natural Language Processing." Journal of Research and Review: Machine Learning 1, no. 1 (2025): 1–10. https://doi.org/10.5281/zenodo.14668189.

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<em>Summarizing large text content is a major challenge in the field of natural language processing (NLP). This project delves into the realm of [1] NLP by exploring the application of the Pegasus model, a state-of-the-art deep learning architecture for book summarization. Pegasus, known for its ability to produce abstract summaries, offers a promising solution for summarizing complex book stories. The project involves fine- tuning the Pegasus model on a diverse set of books spanning various genres and topics. Using advanced NLP techniques, Pegasus carefully analyses the complexity of each boo

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Shetty, Ashik N. "A Unified Flask-Based Framework for Image Text Recognition, Multilingual Translation, and Text Summarization." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 4759–63. https://doi.org/10.22214/ijraset.2025.69051.

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This study presents a comprehensive review of OCR (optical character recognition), Translation, and Object Detection Research from a single image. With the fast advancement of deep learning, more powerful tools that can learn semantic, highlevel, and deeper features have been proposed to solve the issues that plague traditional systems. The rise of high-powered desktop computer has aided OCR reading technology by permitting the creation of more sophisticated recognition software that can read a range of common printed typefaces and handwritten texts. However, implementing an OCR that works in

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Jeba, J. Angelin, S. Rubin Bose, R. Regin, S. Suman Rajest, and Md Mahdi Hasan. "Intelligent Tamil Video Summarization: AI-Powered NLP, Translation, and Speech Integration for Enhanced Accessibility." FMDB Transactions on Sustainable Computer Letters 2, no. 1 (2024): 26–39. http://dx.doi.org/10.69888/ftscl.2024.000179.

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Tamil transcript summarizing improves Tamil video information extraction for study, learning, and accessibility. However, complex grammar, dialects, colloquialisms, and lack of linguistic resources and tools require specific approaches to ensure accurate and successful summarization. A new methodology that smoothly blends natural language processing (NLP), translation, and text-to-speech capabilities is designed to extract crucial insights from abundant internet video footage. The application effectively collects video transcripts using the YouTube Transcript API library and spacy for NLP for

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Patel H, Akshay, Mourya R, Moulya M, Vismitha K B, and Darshan HM. "Image Text to Speech Conversion in Desired Language and Summarization with Raspberry PI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39818.

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In recent years, the integration of artificial intelligence and embedded systems has gained significant traction, enabling the development of efficient and user-friendly solutions. ourproject focuses on building a system capable of extracting text from images, converting the extracted content into speech in a desired language, and providing concise summaries, all powered by a Raspberry Pi. The system employs Optical Character Recognition (OCR) for text extraction, a text-to-speech engine for audio synthesis, and natural language processing techniques for summarization. Designed with accessibil

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Sorokina, Svetlana. "Exploring Automated Summarization: From Extraction to Abstraction." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije 23, no. 5 (2024): 47–59. https://doi.org/10.15688/jvolsu2.2024.5.4.

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This paper provides a review of AI-powered automated summarization models, with a focus on two principal approaches: extractive and abstractive. The study aims to evaluate the capabilities of these models in generating concise yet meaningful summaries and analyze their lexical proficiency and linguistic fluidity. The compression rates are assessed using quantitative metrics such as page, word, and character counts, while language fluency is described in terms of ability to manipulate grammar and lexical patterns without compromising meaning and content. The study draws on a selection of scient

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Bitar, Hind, Amal Babour, Fatema Nafa, Ohoud Alzamzami, and Sarah Alismail. "Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study." International Journal of Environmental Research and Public Health 19, no. 13 (2022): 8100. http://dx.doi.org/10.3390/ijerph19138100.

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Despite the availability of online educational resources about human papillomavirus (HPV), many women around the world may be prevented from obtaining the necessary knowledge about HPV. One way to mitigate the lack of HPV knowledge is the use of auto-generated text summarization tools. This study compares the level of HPV knowledge between women who read an auto-generated summary of HPV made using the BERT deep learning model and women who read a long-form text of HPV. We randomly assigned 386 women to two conditions: half read an auto-generated summary text about HPV (n = 193) and half read a

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வீரக்கண்ணன் எஸ். "Towards a Comprehensive NLP Platform for Tamil: Addressing Spelling, Grammar, Summarization, and Real-time Transcription." Tamilmanam International Research Journal of Tamil Studies 2, no. 01 (2025): 10–18. https://doi.org/10.63300/tm0201202502.

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This study proposes the development of a novel web-based platform leveraging advanced Natural Language Processing (NLP) techniques to address critical needs in Tamil language processing. The platform aims to provide automated solutions for identifying and rectifying spelling and grammar errors, performing contextual text summarization, and enabling real-time speech-to-text transcription of spoken Tamil. While the ultimate objective is a comprehensive user-facing platform, the primary research focus is on the design, development, and implementation of robust underlying machine learning models s

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NISHANTH JOSEPH PAULRAJ. "Natural Language Processing on Clinical Notes: Advanced Techniques for Risk Prediction and Summarization." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 494–502. https://doi.org/10.32996/jcsts.2025.7.3.56.

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This article explores the application of Natural Language Processing (NLP) techniques to clinical notes, focusing specifically on risk prediction and automated summarization capabilities. Healthcare institutions generate vast amounts of unstructured clinical text that contains critical information not captured in structured data fields. It examines how modern NLP approaches, including named entity recognition, text classification, and clinical summarization, can extract actionable insights from narrative documentation. It discusses specialized language models like BioBERT, ClinicalBERT, and Me

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Shriramwar, Dr S. S. "Seamless Cross-Platform Document Conversion Application." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 513–19. https://doi.org/10.22214/ijraset.2025.70058.

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The need for a versatile mobile and web application that integrates document and multimedia conversion, and accessibility features has increased due to advancements of digital technologies. This research presents the design and development of a cross-platform mobile application built using Flutter, utilizing cutting-edge technologies to meet various users’ requirements. The application includes a set of tools such as PDF-to-Word, Word-to-PDF, and PPT-to-PDF converters, images to PDF, XLS-to-PDF, merge PDFs, image-based text extraction through Optical Character Recognition (OCR), speech-to-text

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Li, Liuqing, Jack Geissinger, William A. Ingram, and Edward A. Fox. "Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning." Data and Information Management 4, no. 1 (2020): 18–43. http://dx.doi.org/10.2478/dim-2020-0003.

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AbstractNatural language processing (NLP) covers a large number of topics and tasks related to data and information management, leading to a complex and challenging teaching process. Meanwhile, problem-based learning is a teaching technique specifically designed to motivate students to learn efficiently, work collaboratively, and communicate effectively. With this aim, we developed a problem-based learning course for both undergraduate and graduate students to teach NLP. We provided student teams with big data sets, basic guidelines, cloud computing resources, and other aids to help different

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Shruthi, D., H. K. Chethan, and Victor Ikechukwu Agughasi. "Effective Approach for Fine-Tuning Pre-Trained Models for the Extraction of Texts From Source Codes." ITM Web of Conferences 65 (2024): 03004. http://dx.doi.org/10.1051/itmconf/20246503004.

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This study introduces SR-Text, a robust approach leveraging pre-trained models like BERT and T5 for enhanced text extraction from source codes. Addressing the limitations of traditional manual summarization, our methodology focuses on fine-tuning these models to better understand and generate contextual summaries, thus overcoming challenges such as long-term dependency and dataset quality issues. We conduct a detailed analysis of programming language syntax and semantics to develop syntax-aware text retrieval techniques, significantly boosting the accuracy and relevance of the texts extracted.

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Vasuki, M. "Learning Efficient to Converting a Video Content into Summarizing Word Document Using NLP." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5546–49. http://dx.doi.org/10.22214/ijraset.2024.62245.

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Abstract: This paper explores an efficient methodology for converting video content into summarized Word documents using Optical Character Recognition (OCR) tools. The process integrates multiple advanced technologies, including frame extraction, audio transcription, OCR for text recognition in video frames, and natural language processing (NLP) techniques for text summarization. The proposed approach begins with extracting key frames from the video and applying OCR to capture onscreen text. Simultaneously, the video's audio track is transcribed into text using speech-to-text technology. The e

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Heena, Girdher*, and Gaur Poonam. "TEXT MINING TECHNIQUES-A REVIEW." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 6 (2017): 68–73. https://doi.org/10.5281/zenodo.817359.

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Text mining is a technology that is used to extract meaningful information from unstructured or semi structured text. The amount of data is increasing at tremendous speed. So there is a need to extract meaningful information from huge amount of data. Text mining techniques are used for this purpose. This paper focuses on text mining process, various techniques of text mining. In addition to this we have also discussed a comparison between text mining techniques on the basis of Goal, Algorithms and Tools.

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Caccamise, Donna. "Improved Reading Comprehension by Writing." Perspectives on Language Learning and Education 18, no. 1 (2011): 27–31. http://dx.doi.org/10.1044/lle18.1.27.

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In this article, the author argues that writing skills are important in developing reading comprehension skills that prepare our students for the advanced studies of the 21st Century. Examples are provided of available writing tools/interventions that may help teachers provide more writing practice that makes this connection to improved reading comprehension. One example discussed is Summary Street, which focuses on the process of summarization. Summarizing is an important comprehension skill that helps readers develop the gist of their reading and form a lasting and accurate text base represe

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Glenc, Piotr. "Narzędzia do automatycznego streszczania tekstów w języku polskim. Stan badań naukowych i prac wdrożeniowych." e-mentor 89, no. 2 (2021): 67–77. http://dx.doi.org/10.15219/em89.1513.

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The goal of the publication is to present the state of research and works carried out in Poland on the issue of automatic text summarization. The author describes principal theoretical and methodological issues related to automatic summary generation followed by the outline of the selected works on the automatic abstracting of Polish texts. The author also provides three examples of IT tools that generate summaries of texts in Polish (Summarize, Resoomer, and NICOLAS) and their characteristics derived from the conducted experiment, which included quality assessment of generated summaries using

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K, Sakkaravarthy Iyyappan, and Balasundaram SR. "A Multi Document Summarization of Learning Materials using Bigram Embedding Technique and Integer Linear Programming." International Journal of Membrane Science and Technology 10, no. 2 (2023): 3450–56. http://dx.doi.org/10.15379/ijmst.v10i2.3149.

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In the present era of the Internet, teachers and learners are heavily inclined to use e-learning systems for an efficient learning process. Due to the proliferation of educational text contents in these e-learning systems, the need for incorporating advanced text analysis tools and techniques are becoming inevitable. Multi Document Summarization (MDS) is a technique for producing concise summaries from a collection of related text documents. The usage of MDS in the context of e-learning is more promising for providing summaries for learning materials which helps students and teachers to focus

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Sarol, M. Janina, Ly Dinh, and Jana Diesner. "Variation in Situational Awareness Information due to Selection of Data Source, Summarization Method, and Method Implementation." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 597–608. http://dx.doi.org/10.1609/icwsm.v15i1.18087.

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The increasing amount of information available about crisis events calls for tools that help people to efficiently and reliably sift through and summarize this information. When using such tools, practitioners have to make choices that can affect not only the relevant information retrieved, but also their understanding or situational awareness of a crisis. We present a study that assesses the impact of some of these choices on the resulting situational awareness information. We focus on commonly used sources (news, tweets, and blogs), text mining methods (topic modeling and text summarization)

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Yu, Wenhui, Gengshen Wu, and Jungong Han. "Deep Multimodal-Interactive Document Summarization Network and Its Cross-Modal Text–Image Retrieval Application for Future Smart City Information Management Systems." Smart Cities 8, no. 3 (2025): 96. https://doi.org/10.3390/smartcities8030096.

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Urban documents like city planning reports and environmental data often feature complex charts and texts that require effective summarization tools, particularly in smart city management systems. These documents increasingly use graphical abstracts alongside textual summaries to enhance readability, making automated abstract generation crucial. This study explores the application of summarization technology using scientific paper abstract generation as a case. The challenge lies in processing the longer multimodal content typical in research papers. To address this, a deep multimodal-interacti

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Nur Fitria, Tira. "Using Manual and Automatic Summarization: What Should Students Consider in Writing a Summary?" English Language and Education Spectrum 3, no. 2 (2023): 85–105. http://dx.doi.org/10.53416/electrum.v3i2.140.

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Writing a summary related to reading and writing. Writing a summary of a text not only helps students assimilate what they have read by highlighting and connecting the key points but also enables them to articulate their thoughts in writing. It means that a summary must be brief, accurate, and written in individual words. This study describes teaching summarization both manual and automatic summarization for students. This research is library research. The analysis shows that writing and summarizing a journal article is a common task for students. Students can read articles for summaries, plan

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R, Sivaranjani. "AI Call Assistant – Extractive Summarization of Call Recordings." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32324.

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In today's fast-paced business landscape, effective communication is a key for success. With the increasing volume of phone calls in various industries, there is a growing need for efficient call management and analysis tools. The AI Call Assistant project aims to address this need by leveraging artificial intelligence (AI) technology to summarize call recordings, enhancing productivity and decision-making processes. The core functionality of this project revolves around its ability to process audio data from call recordings and extract key insights and information. Using advanced natural lang

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