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Journal articles on the topic 'Natural Processing Language (NLP) Natural Language Tookkit (NLTK) Extractive Summarization Abstractive Summarization'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

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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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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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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Kirmani, Mahira, Gagandeep Kaur, and Mudasir Mohd. "Analysis of Abstractive and Extractive Summarization Methods." International Journal of Emerging Technologies in Learning (iJET) 19, no. 01 (2024): 86–96. http://dx.doi.org/10.3991/ijet.v19i01.46079.

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This paper explains the existing approaches employed for (automatic) text summarization. The summarizing method is part of the natural language processing (NLP) field and is applied to the source document to produce a compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text-based summarization are categorized into two groups: abstractive and extractive. In abstractive summarization, the main contents of the input text are paraphrased, possibly using vocabulary that is not present in the source document, while in extractive summarization, th

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

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

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Karnan, K., and Dr L. R. Aravind Babu. "Text Mining and Natural Language Processing Frameworks for ‎Enhanced Fake News Detection, Sentiment Analysis, and ‎Automated Summarization in Social Media." International Journal of Basic and Applied Sciences 14, no. 2 (2025): 107–12. https://doi.org/10.14419/hgj17c14.

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Efficient text summarization, public sentiment analysis, and fake news detection have become difficult tasks due to the exponential growth of ‎digital content. Sentiment analysis aids in assessing trends and public opinion, while fake news detection is crucial for combating false ‎information. To alleviate information overload, automated text summarization extracts important information from long documents. This ‎study examines three sophisticated Natural Language Processing (NLP) models: 1) The BiLSTM-based sentiment analysis model uses ‎Word2Vec embeddings and bidirectional LSTM units to und

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Hassan, Abdulkhaleq Q. A., Badriyya B. Al-onazi, Mashael Maashi, Abdulbasit A. Darem, Ibrahim Abunadi, and Ahmed Mahmud. "Enhancing extractive text summarization using natural language processing with an optimal deep learning model." AIMS Mathematics 9, no. 5 (2024): 12588–609. http://dx.doi.org/10.3934/math.2024616.

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<abstract> <p>Natural language processing (NLP) performs a vital function in text summarization, a task targeted at refining the crucial information from the massive quantity of textual data. NLP methods allow computers to comprehend and process human language, permitting the development of advanced summarization methods. Text summarization includes the automatic generation of a concise and coherent summary of a specified document or collection of documents. Extracting significant insights from text data is crucial as it provides advanced solutions to end-users and business organiz

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Helen, Afrida. "Automatic Abstractive Summarization Task for New Article." EMITTER International Journal of Engineering Technology 6, no. 1 (2018): 22–34. http://dx.doi.org/10.24003/emitter.v6i1.212.

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Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the statistical method and the Natural Processing Language (NLP) method. Statistical method produce Extractive summary that the summaries consist of independent sentences considered important content of document. Unfortunately, the coherence of the summary is poor. Besides that, the Natural Processing Language expected can produces summary w

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Shilpa Serasiya. "Abstractive Gujarati Text Summarization Using Sequence-To-Sequence Model and Attention Mechanism." Journal of Information Systems Engineering and Management 10, no. 41s (2025): 754–62. https://doi.org/10.52783/jisem.v10i41s.7998.

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Introduction: In recent years text summarization has been one of the piloting problems of natural language processing (NLP). It comprises a consolidated brief on a large text document. Extractive and Abstractive are the two output-based summarization techniques. For the Indian Language much research is being carried out in Extractive Summarization. Performance of Abstractive summarization remains a challenge for a language like Gujarati. With the rise of digital Gujarati news portals, automatic summarization can provide concise versions of news articles and make it easier for readers to grasp

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Saklecha, Aahana, Pragya Uplavdiya, and Prof M. P. S. Chawla. "An Extensive Survey on Investigation Methodologies for Text Summarization." Indian Journal of Signal Processing 3, no. 4 (2023): 1–6. http://dx.doi.org/10.54105/ijsp.d1016.113423.

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Natural language processing (NLP) is a fast-expanding field, and text summarization has recently gained a lot of research interest. The necessity for automatic summarizing approaches to effectively digest massive amounts of textual data has grown in importance, due to the plethora (excessive amount of something) of information available in the digital age [18]. By automatically producing succinct and educational summaries of extensive materials, NLP-based text summarizing systems have the potential to revolutionize the way humans consume and process information. This review paper offers a thor

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Chaitanya, DR D. Eswara. "ADVANCMENTS IN TEXT SUMMARIZATION AND EXTRACTIVE QUESTION- ANSWERING : A MACHINE LEARNING APPROACH." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31446.

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In the era of social media platforms, the rapid expansion of data mining in the fields of information retrieval and natural language processing emphasizes the crucial need for automated text summarization. At the current time, pretrained word embedding techniques and sequence to sequence models can be effectively repurposed in the realm of social network summarization to efficiently condense significant information with strong encoding capabilities. However, dealing with the challenge of extended text dependency and efficiently utilizing latent topic mapping presents an increasingly significan

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Manisha Gaikwad,. "“An Extensive study of Symantic and Syntatic Approaches to Automatic Text Summarization”." Journal of Electrical Systems 20, no. 1s (2024): 455–68. http://dx.doi.org/10.52783/jes.785.

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Automatic text summarization (ATS) has emerged as a crucial research domain in the discipline of natural language processing (NLP) and information retrieval. The exponential growth of digital content has necessitated the need for efficient techniques that can automatically generate concise and informative summaries from lengthy documents. This article provided a comprehensive recap of automatic text summarization, covering both abstractive and extractive methods. Using extractive techniques, prime phrases or keywords from the original text are identified and chosen, while abstractive methods i

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Anuradha, Surabhi, and Martha Sheshikala. "Investigating the recall efficiency in abstractive summarization: an experimental based comparative study." Indonesian Journal of Electrical Engineering and Computer Science 39, no. 1 (2025): 446. https://doi.org/10.11591/ijeecs.v39.i1.pp446-454.

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<p>This study explores text summarization, a critical component of natural language processing (NLP), specifically targeting scientific documents. Traditional extractive summarization, which relies on the original wording, often results in disjointed sequences of sentences and fails to convey key ideas concisely. To address these issues and ensure comprehensive inclusion of relevant details, our research aims to improve the coherence and completeness of summaries. We employed 25 different large language models (LLMs) to evaluate their performance in generating abstractive summaries of sc

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

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

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Karnik, Madhuri P., and D. V. Kodavade. "Abstractive Summarization with Efficient Transformer Based Approach." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 291–98. http://dx.doi.org/10.17762/ijritcc.v11i4.6454.

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One of the most significant research areas is how to make a document smaller while keeping its essential information because of the rapid proliferation of online data. This information must be summarized in order to recover meaningful knowledge in an acceptable time. Text summarization is what it's called. Extractive and abstractive text summarization are the two types of summarization. In current years, the arena of abstractive text summarization has become increasingly popular. Abstractive Text Summarization (ATS) aims to extract the most vital content from a text corpus and condense it into

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Sharma, Dimpy, Tushar Soni, and Ruchi Patira. "AI-Driven Text Summarization for Efficient Information Processing." International Journal of Innovations & Research Analysis 05, no. 02 (2025): 19–26. https://doi.org/10.62823/ijira/5.2.7435.

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Text summarization is a fundamental task in Natural Language Processing (NLP) that aims to condense large volumes of text into concise, meaningful summaries while preserving essential information. As digital content continues to grow exponentially, automated summarization techniques have gained prominence in various domains, including finance, healthcare, law, education, and journalism. By enabling faster information retrieval and improved decision-making, AI-driven summarization is transforming data-intensive industries. Summarization techniques can be broadly classified into extractive and a

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Gothankar, Ajinkya, Lavish Gupta, Niharika Bisht, Samiksha Nehe, and Prof Monali Bansode. "Extractive Text and Video Summarization using TF-IDF Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 927–32. http://dx.doi.org/10.22214/ijraset.2022.40775.

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Abstract: Text summarization is a technique for extracting concise summaries from a large text without sacrificing any important information. It's a good way to extract crucial information from documents. The rapid rise of the internet has resulted in a substantial surge in data all across the world. It has become difficult for humans to manually summarise big documents. Automatic Text Summarization is an NLP technique that lowers the time and efforts required by a human to create a summary. Text summarising techniques are divided into two categories: extractive and abstractive. In the extract

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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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Mobin, MD Iftekharul, Mahamodul Hasan Mahadi, Al-Sakib Khan Pathan, and A. F. M. Suaib Akhter. "A Review of the State-of-the-Art Techniques and Analysis of Transformers for Bengali Text Summarization." Big Data and Cognitive Computing 9, no. 5 (2025): 117. https://doi.org/10.3390/bdcc9050117.

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Text summarization is a complex and essential task in natural language processing (NLP) research, focused on extracting the most important information from a document. This study focuses on the Extractive and Abstractive approaches of Bengali Text Summarization (BTS). With the breakthrough advancements in deep learning, summarization is no longer a major challenge for English, given the availability of extensive resources dedicated to this global language. However, the Bengali language remains underexplored. Hence, in this work, a comprehensive review has been conducted on BTS research from 20

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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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Reddy C, Kishor Kumar, P. R. Anisha, Nhu Gia Nguyen, and G. Sreelatha. "A Text Mining using Web Scraping for Meaningful Insights." Journal of Physics: Conference Series 2089, no. 1 (2021): 012048. http://dx.doi.org/10.1088/1742-6596/2089/1/012048.

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Abstract This research involves the usage of Machine Learning technology and Natural Language Processing (NLP) along with the Natural Language Tool-Kit (NLTK). This helps develop a logical Text Summarization tool, which uses the Extractive approach to generate an accurate and a fluent summary. The aim of this tool is to efficiently extract a concise and a coherent version, having only the main needed outline points from the long text or the input document avoiding any type of repetitions of the same text or information that has already been mentioned earlier in the text. The text to be summari

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Neda, Alipour, and Aydın Serdar. "Abstractive summarization using multilingual text-to-text transfer transformer for the Turkish text." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1587–96. https://doi.org/10.11591/ijai.v14.i2.pp1587-1596.

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Today, with the increase in text data, the application of automatic techniques such as automatic text summarization, which is one of the most critical natural language processing (NLP) tasks, has attracted even more attention and led to more research in this area. Nowadays, with the developments in deep learning, pre-trained sequence-to-sequence (text-to-text transfer converter (T5) and bidirectional encoder representations from transformers (BERT) algorithm) encoder-decoder models are used to obtain the most advanced results. However, most of the studies were done in the English language. Wit

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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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Matimpati Chitra Rupa and Kasarapu Ramani. "Hybrid Approaches for Advanced Medical Text Summarization: Combining TF-IDF, BERT, and Seq2Seq Models." Advance Sustainable Science Engineering and Technology 7, no. 3 (2025): 0250301. https://doi.org/10.26877/reh2an46.

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Clinicians, researchers, and healthcare professionals are confronted with the challenge of efficiently extracting relevant knowledge from vast amounts of textual data. Medical text summarization emerges as a crucial tool to address this challenge by condensing lengthy medical documents into concise, informative summaries. A comprehensive hybrid approach is proposed to address the challenges in medical text summarization by combining both extractive and abstractive methods, by integrating Term Frequency-Inverse Document Frequency (TF-IDF) of Natural Language Processing (NLP) and AutoModelForSeq

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Alipour, Neda, and Serdar Aydın. "Abstractive summarization using multilingual text-to-text transfer transformer for the Turkish text." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1587. https://doi.org/10.11591/ijai.v14.i2.pp1587-1596.

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<span lang="EN-US">Today, with the increase in text data, the application of automatic techniques such as automatic text summarization, which is one of the most critical natural language processing (NLP) tasks, has attracted even more attention and led to more research in this area. Nowadays, with the developments in deep learning, pre-trained sequence-to-sequence (text-to-text transfer converter (T5) and bidirectional encoder representations from transformers (BERT) algorithm) encoder-decoder models are used to obtain the most advanced results. However, most of the studies were done in

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Dmitrieva, K. A., and M. R. Zholus. "Automatic Summarization of Parental Chats on WhatsApp." NSU Vestnik. Series: Linguistics and Intercultural Communication 23, no. 1 (2025): 80–92. https://doi.org/10.25205/1818-7935-2025-23-1-80-92.

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Automatic text summarization is one of the main tasks of natural language processing (NLP), which consists in creating a shorter version of the source text. In today’s world the amount of information consumed by people is constantly increasing, therefore more and more emphasis is being placed on the task of summarization. There are two main approaches to automatic text summarization: extractive and abstractive ones. The latter involves automatic creation of a summary text that may contain words and phrases not present in the source. This approach usually requires the usage of AI models, which

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Dangol, Raksha, Prashna Adhikari, Pranjal Dahal, and Hrizu Sharma. "Short Updates- Machine Learning Based News Summarizer." Journal of Advanced College of Engineering and Management 8, no. 2 (2023): 15–25. http://dx.doi.org/10.3126/jacem.v8i2.55939.

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Automated Text Summarization is becoming important due to the vast amount of data being generated. Manual processing of documents is tedious, mostly due to the absence of standards. Therefore, there is a need for a mechanism to reduce text size, structure it, and make it readable for users. Natural Language Processing (NLP) is critical for analyzing large amounts of unstructured, text-heavy data. This project aims to address concerns with extractive and abstractive text summarization by introducing a new neural network model that deals with repetitive and incoherent phrases in longer documents

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. Srinivasa Kumar, Dr C. "YouTube Transcript Summarizer." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50723.

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With the ever-increasing volume of video content uploaded to YouTube daily, users often struggle to find and consume relevant information efficiently. To address this challenge, we developed a YouTube Transcript Summarizer that automatically generates concise summaries from video transcripts. The system begins by extracting the transcript from a provided YouTube URL, then processes the text using natural language processing (NLP) techniques such as tokenization, stop-word removal, and sentence segmentation. After preprocessing, the system applies a combination of extractive and abstractive sum

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Chafiq, Nadia, Mohamed Ghazouani, and Rokaya El Gounidi. "From Manual Review to AI Automation: An NLP-Powered System for Efficient CV Processing in Academic Admissions." LatIA 3 (May 20, 2025): 315. https://doi.org/10.62486/latia2025315.

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Manual screening of thousands of admissions of master's program applications at Hassan II University of Casablanca is a time and labor-intensive task. Towards this challenge, we designed a machine-based solution utilizing Natural Language Processing (NLP) for summarization and CV ranking on a large set of CVs. Our solution relies on pre-trained spaCy and Hugging Face Transformers-based Named Entity Recognition (NER) models for the retrieval of information such as education, experience, and skills. We then incorporated extractive summarization by using BERT-based models for the selection of the

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Joshi, Manju Lata, Nisheeth Joshi, and Namita Mittal. "SGATS: Semantic Graph-based Automatic Text Summarization from Hindi Text Documents." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (2021): 1–32. http://dx.doi.org/10.1145/3464381.

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Creating a coherent summary of the text is a challenging task in the field of Natural Language Processing (NLP). Various Automatic Text Summarization techniques have been developed for abstractive as well as extractive summarization. This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning, Latent Semantic An

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Bhargavi, A. D. "Video Transcripts Summarization using OpenAI Whisper and GPT Model." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2319–27. http://dx.doi.org/10.22214/ijraset.2024.59365.

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Abstract: In today’s digital age, a vast amount of video content is generated and shared on the internet every minute. However, extracting relevant information from these videos can be time-consuming and challenging. This is where video transcript summarization comes in, providing a concise summary of video content without the need to watch the entire video. The video transcript summarization system aims to streamline the process of extracting key insights and information from video content by generating concise and informative summaries from their transcripts. In the dynamic landscape of vide

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Wazery, Y. M., Marwa E. Saleh, Abdullah Alharbi, and Abdelmgeid A. Ali. "Abstractive Arabic Text Summarization Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (January 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/1566890.

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Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system’s capabilities with all its new improvement. Many papers and research studies address this task in literature but are being carried out in extractive summarization, and few of them are being carried out in abstractive summarization, especially in the Arabic language due to its complexity. In this paper, an abstractive Arabic text summarization system is proposed, based on a sequence-to-sequence model

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

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

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Dhanda, Namrata, and Kapil Kumar Gupta. "A Novel Approach to Text Summarization Using Machine Learning." Asian Journal of Research in Computer Science 17, no. 4 (2024): 95–104. http://dx.doi.org/10.9734/ajrcos/2024/v17i4432.

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Text summarization is a key strategy in the domains of information retrieval and natural language processing (NLP). Its objective is to reduce a lengthy written document into a clearer, more succinct summary of the information it contains. When a text document is too lengthy or intricate to analyse completely, as in news stories, academic papers, or legal documents, this approach is extremely helpful. The major challenge of text summarising is to take the most important and relevant information from the original text and convey it in an understandable and concise way. In this study, extractive

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Verma, Jai Prakash, Shir Bhargav, Madhuri Bhavsar, et al. "Graph-Based Extractive Text Summarization Sentence Scoring Scheme for Big Data Applications." Information 14, no. 9 (2023): 472. http://dx.doi.org/10.3390/info14090472.

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The recent advancements in big data and natural language processing (NLP) have necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous textual data. Text summarization (TS) acts as an essential pillar within recommendation engines. Despite the prevalent use of abstractive techniques in TS, an anticipated shift towards a graph-based extractive TS (ETS) scheme is becoming apparent. The models, although simpler and less resource-intensive, are key in assessing reviews and feedback on products or services. Nonetheless, current methodologies have not fully resolve

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Rifat Mustafa, Shabana Rai, Ubaid Ullah, and Muhammad Sohaib Naz. "Summary in General Summary of an Overview of Opinion Mining." Journal of Advancement in Computing 1, no. 1 (2023): 9–13. http://dx.doi.org/10.36755/jac.v1i1.47.

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The internet is a very effective resource for solving all problems in the present era. The world's population as a whole spends one-third of their time and money using the internet. People learn things from it in every aspect of life, including education, entertainment, communication, shopping, etc. In order to achieve this, consumers take use of websites and share comments or opinions about various goods, services, events, etc. based on their personal experiences. In this way, the input from those webs is composed into a sizable amount of textual data that can be investigated, assessed, and c

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Bharti, Deshmukh. "TEXT SUMMARIZATION USING PYTHON NLTK." June 15, 2022. https://doi.org/10.5281/zenodo.6797853.

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Text summarization is basically summarizing of the given paragraph with the use of natural language processing and machine learning. There has been an explosion in the quantity of textual content records from lot of sources. This quantity of textual content is a useful supply of facts and information which needs to be efficiently summarized to be useful. In this paper, the primary tactics to computerized textual content summarization were described. The distinctive approaches for summarization and the effectiveness and shortcomings of the distinctive methods were described. The machi

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"GUI Based Text Summarizing of Social Response." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (2020): 1773–76. http://dx.doi.org/10.35940/ijitee.d1710.029420.

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Text Summarization is one of those utilizations of Natural Language Processing (NLP) which will undoubtedly hugy affect our lives. For the most part, Text outline can comprehensively be partitioned into two classifications, Extractive Summarization and Abstractive Summarization and the execution of seq2seq model for rundown of literary information utilizing of tensor stream/keras and showed on amazon or social reaction surveys, issues and news stories. Content rundown is a subdomain of Natural Language Processing that manages removing synopses from tremendous lumps of writings. There are two f

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Chintan A. Shah and Prof. Neelam Phadnis. "Text Summarization using Extractive and Abstractive Techniques." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, May 10, 2022, 236–41. http://dx.doi.org/10.32628/cseit228361.

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There have been considerable advancements in Text Summarization over the last few years. There are two ways to text summarization: one is based on natural language processing (NLP), and the other is based on deep learning. In the realm of NLP, text summarization is the most intriguing and challenging task. NLP stands for Natural Language Processing, which studies and manipulates human language by computers. Because of the massive increase in information and data, it has become critical. Text summarization is creating a thorough and meaningful summary of text from various sources such as books,

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"Extractive and Abstractive Text Summarization Techniques." International Journal of Recent Technology and Engineering 9, no. 1 (2020): 1040–44. http://dx.doi.org/10.35940/ijrte.a2235.059120.

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Text summarization generates an abstract version of information on a particular topic from various sources without modifying its originality. It is essential to dig information from the large repository of data, thereby eliminating the irrelevant information. The manual summarization consumes a large amount of time and hence an automated text summarization model is required. The summarization can be performed from a single source or multiple sources. The Natural Language Processing (NLP) based text summarization can be generally categorized as abstractive and extractive methods. The extractive

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Serasiya, Shilpa, and Uttam Chauhan. "A Comprehensive Survey on Text Summarization For Indian Languages: Opportunities, Challenges and Future Prospects." South Eastern European Journal of Public Health, February 22, 2025, 1418–34. https://doi.org/10.70135/seejph.vi.4957.

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An increasing quantum of data available on the web, news websites, published articles in various fields of study, and electronic books have generated a valuable resource for extracting and analyzing information. The main challenge for researchers has been that of accessing accurate and reliable data. This information must be summarized to retrieve helpful knowledge within a reasonable period. Text summarization is a crucial Natural Language Processing (NLP) task that aims to condense lengthy documents into shorter, coherent summaries while retaining the essential information. Text summarizatio

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"Recent Trends in Deep Learning Based Abstractive Text Summarization." International Journal of Recent Technology and Engineering 8, no. 3 (2019): 3108–15. http://dx.doi.org/10.35940/ijrte.c4996.098319.

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With the rapid growth of cyberspace and the appearance of knowledge exploration era, good text summarization method is vital to reduce the large data. Text summarization is the mechanism of extracting the important information which gives us an overall abstract or summary of the entire document and also reduces the size of the document. It is open problem in Natural Language Processing (NLP) and a difficult work for humans to understand and generate an abstract manually while it have need of a accurate analysis of the document. Text Summarization has become an important and timely tool for ass

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Aggarwal, Kartik. "A Review of Text Summarization Techniques Using NLP." Computational Intelligence and Machine Learning 4, no. 2 (2023). http://dx.doi.org/10.36647/ciml/04.02.a001.

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Techniques that employ natural language processing (NLP), often known as text summarizing, automatically construct summaries of extensive texts. Extractive and abstractive summarization are two main categories that may be used to classify these methods. In extractive summarizing, the most significant lines or phrases from a text are isolated and used to generate a summary. On the other hand, in abstractive summarization, a summary is generated that is clear, short, and accurate in its representation of the text's primary concepts. NLP methods like sentence segmentation, part-of-speech tagging,

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Agate, Vrushali, Shilotej Mirajkar, and Gauri Toradmal. "Book Summarization using NLP." International Journal of Innovative Research in Engineering, April 11, 2023, 476–80. http://dx.doi.org/10.59256/ijire.2023040218.

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It is crucial to offer an enhanced system for swiftly and effectively extracting information in this modern era where the Internet is a wealth of knowledge. It is quite challenging for humans to manually extract the summary from a lengthy written document. On the Internet, there is a wealth of textual content. As a result, finding relevant papers from the many that are available and learning useful information from them is a challenge. An automatic text summary is crucial for resolving the two issues. The technique of extracting the most significant information from a document or group of rela

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Prakash, Nayana Cholanayakanahalli, Achyutha Prasad Narasimhaiah, Jagadeesh Bettakote Nagaraj, Piyush Kumar Pareek, Rekha Vasudev Sedam, and Nirmala Govindhaiah. "survey on NLP based automatic extractive text summarization using spacy." International journal of health sciences, July 9, 2022, 1514–25. http://dx.doi.org/10.53730/ijhs.v6ns8.10526.

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Automatic abstraction of text is considered one of the most difficult problems because mathematically there is no real way to test a summary but one can distinguish between the appropriate summary. Also, abbreviations may be of a variety of abstractive forms in which new phrases and terms are used, in contrast Extractive form in which scoring sentences from enter textual content gets extracted as a summary sentence. The growing discovery of online facts has necessitated extensive research within the region of automated text within the Natural language processing (NLP). Over the course of the y

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Prakash, Nayana Cholanayakanahalli, Achyutha Prasad Narasimhaiah, Jagadeesh Bettakote Nagaraj, Piyush Kumar Pareek, Rekha Vasudev Sedam, and Nirmala Govindhaiah. "survey on NLP based automatic extractive text summarization using spacy." International journal of health sciences, July 9, 2022. http://dx.doi.org/10.53730/ijhs.v6ns4.10526.

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Automatic abstraction of text is considered one of the most difficult problems because mathematically there is no real way to test a summary but one can distinguish between the appropriate summary. Also, abbreviations may be of a variety of abstractive forms in which new phrases and terms are used, in contrast Extractive form in which scoring sentences from enter textual content gets extracted as a summary sentence. The growing discovery of online facts has necessitated extensive research within the region of automated text within the Natural language processing (NLP). Over the course of the y

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Younis, Mohammed Hashim, and Ibrahim M. I. Zebari. "Enhancing Medical Text Summarization using Transformer-Based NLP Models for Clinical Decision Support." Engineering and Technology Journal 10, no. 05 (2025). https://doi.org/10.47191/etj/v10i05.55.

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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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Shahapure, Ketan, Samit Shivadekar, Shivam Vibhute, and Milton Halem. "Automated Text Summarization as A Service." International Journal of Scientific Research in Science, Engineering and Technology, January 2, 2024, 54–65. http://dx.doi.org/10.32628/ijsrset12310669.

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Recent advancements in technology have enabled the storage of voluminous data. As this data is abundant, there is a need to create summaries that would capture the relevant details of the original source. Since manual summarization is a very taxing process, researchers have been actively trying to automate this process using modern computers that could try to comprehend and generate natural human language. Automated text summarization has been one of the most researched areas in the realm of Natural Language Processing (NLP). Extractive and abstractive summarization are two of the most commonl

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