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Academic literature on the topic 'Natural Processing Language (NLP) Natural Language Tookkit (NLTK) Extractive Summarization Abstractive Summarization'
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Journal articles on the topic "Natural Processing Language (NLP) Natural Language Tookkit (NLTK) Extractive Summarization Abstractive Summarization"
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Karunamurthy, Dr A., R. Ramakrishnan, J. Nivetha, and S. Varsha. "Auto Synopsis: An Intelligent Web-Based Application for Automating Content Summarization Using Advanced NLP Techniques." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–6. https://doi.org/10.55041/isjem02157.
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Auto Synopsis introduces an efficient web-based application designed to automate text summarization using advanced natural language processing (NLP) techniques. Built with Flask, the system extracts and processes textual content, transforming it into concise, meaningful summaries. The text undergoes preprocessing steps, including tokenization, lemmatization, and stemming, to prepare it for analysis. Auto Synopsis supports both extractive and abstractive summarization. Extractive summarization selects and extracts important sentences or segments from the original text, while abstractive summarization generates new sentences that convey core ideas in a more natural, human-like form. For smaller documents, a sentence similarity approach using cosine distance ranks sentences based on relevance. For larger documents, the PageRank algorithm evaluates sentence importance to select the most significant content. Auto Synopsis features a secure user authentication system, allowing individuals to create accounts, log in, and access personalized summaries. Designed for students, researchers, and professionals, this tool aims to streamline the summarization process, helping users quickly extract essential information from lengthy text. By reducing reading time and enhancing productivity, Auto Synopsis provides an invaluable solution for efficiently processing large volumes of information, ensuring that users gain quick and meaningful insights from complex documents. Keywords: Text Summarization, Automatic Summarization, Extractive Summarization, Abstractive Summarization, Natural Language Processing, Flask Web Application, PageRank Algorithm
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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-small model (Raffel et al., 2020) for generating concise summaries with minimal computational overhead, while Hugging Face transformers (Wolf et al., 2020) enable sentiment analysis for user feedback interpretation. Emphasizing low-connectivity usage, the architecture supports local deployment of NLP models (Anastasopoulos et al., 2021) and utilizes Flask (Kumar & Singh, 2021) for integrating NLP services into a user-friendly offline web application. Further, the deployment of compressed models on edge devices (Chen et al., 2022) highlights the feasibility of delivering robust summarization and analysis tools without reliance on cloud infrastructure. This work provides a modular, efficient, and accessible framework for document understanding in offline scenarios.
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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 attention because of self-supervised training while fine-tuning for Natural Language Processing (NLP) downstream task like text summarization. The proposed work is an attempt to investigate the use of transformers for abstraction. The proposed work is tested for book especially as a long document for evaluating the performance of the model.
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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, the output summary is a subset of the input text and is generated by using the sentence ranking technique. In this paper, the main ideas behind the existing methods used for abstractive and extractive summarization are discussed broadly. A comparative study of these methods is also highlighted.
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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 to its complexity. This study aims to enhance automated abstractive summarization for Indonesian-language online news articles by employing the PEGASUS (Pre-training with Extracted Gap-sentences Sequences for Abstractive Summarization) model, which leverages an encoder-decoder architecture optimized for summarization tasks. The dataset utilized consists of 193,883 articles from Liputan6, a prominent Indonesian news platform. The model was fine-tuned and evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric, focusing on F-1 scores for ROUGE-1, ROUGE-2, and ROUGE-L. The results demonstrated the model's ability to generate coherent and informative summaries, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.439, 0.183, and 0.406, respectively. These findings underscore the potential of the PEGASUS model in addressing the challenges of abstractive summarization for low-resource languages like Indonesian language, offering a significant contribution to summarization quality for online news content.
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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 the different approaches to text summarization, including extractive, abstractive, and hybrid methods, highlighting their strengths and weaknesses. We discuss various evaluation metrics and datasets commonly used for benchmarking summarization systems, shedding light on the challenges and opportunities in this field.
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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 understand context better and classify text into positive, negative, or neutral sentiments. 2) Followed by a sigmoid classifier, to differentiate real from fake news, the BiLSTM-CNN-based fake news detection model combines a 1D CNN for spatial pattern recognition and BiLSTM for sequential feature extraction. 3) For extractive summarization, the hybrid extractive-abstractive summarization model uses TF-IDF-based sentence weighting for abstractive summarization it uses a Transformer-based encoder-decoder. The outcome is measured using metrics like BLEU and ROUGE. These models improve the online user experience , decision-making, and misinformation detection in text mining applications.
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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 organizations. Automatic text summarization (ATS) computerizes text summarization by decreasing the initial size of the text without the loss of main data features. Deep learning (DL) approaches exhibited significant performance in abstractive and extractive summarization tasks. This research designed an extractive text summarization using NLP with an optimal DL (ETS-NLPODL) model. The major goal of the ETS-NLPODL technique was to exploit feature selection with a hyperparameter-tuned DL model for summarizing the text. In the ETS-NLPODL technique, an initial step of data preprocessing was involved to convert the input text into a compatible format. Next, a feature extraction process was carried out and the optimal set of features was chosen by the hunger games search optimization (HGSO) algorithm. For text summarization, the ETS-NLPODL model used an attention-based convolutional neural network with a gated recurrent unit (ACNN-GRU) model. Finally, the mountain gazelle optimization (MGO) algorithm was employed for the optimal hyperparameter selection of the ACNN-GRU model. The experimental results of the ETS-NLPODL system were examined under the benchmark dataset. The experimentation outcomes pointed out that the ETS-NLPODL technique gained better performance over other methods concerning diverse performance measures.</p> </abstract>
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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 where sentences in summary should not be taken from sentences in the document, but come from the person making the summary. So, the summaries closed to human-summary, coherent and well structured. This study discusses the tasks of generating summary. The conclusion is we can find that there are still opportunities to develop better outcomes that are better coherence and better accuracy.
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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 key information quickly Objectives: We aim to create an effective and efficient abstractive text summarizer for Gujarati text, which can generate an understandable and expressive summary. Methods: Our model works as a Sequence-to-Sequence model using encoder-decoder architecture with an attention mechanism. LSTM-based encoder-decoder with an attention-based model generates human-like sentences with core information of the original documents. Results: Our experiment conducted the effectiveness and success of the proposed model by increasing the accuracy up to 87% and decreasing the loss to 0.48 for the Gujarati Text. Novelty: In terms of NLP, Gujarati is a low-resource language for researchers, especially for text summarization. So to achieve our goal, we created our dataset by collecting Gujarati text data such as news articles and their headlines from online/offline resources like daily newspapers. Gujarati has unique grammatical structures and morphology, so for pre-processing the Gujarati text, we proposed a pre-processor(GujProc) specific to Gujarati to trace the linguistic.
Dissertations / Theses on the topic "Natural Processing Language (NLP) Natural Language Tookkit (NLTK) Extractive Summarization Abstractive Summarization"
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Bergkvist, Alexander, Nils Hedberg, Sebastian Rollino, and Markus Sagen. "Surmize: An Online NLP System for Close-Domain Question-Answering and Summarization." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412247.
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The amount of data available and consumed by people globally is growing. To reduce mental fatigue and increase the general ability to gain insight into complex texts or documents, we have developed an application to aid in this task. The application allows users to upload documents and ask domain-specific questions about them using our web application. A summarized version of each document is presented to the user, which could further facilitate their understanding of the document and guide them towards what types of questions could be relevant to ask. Our application allows users flexibility with the types of documents that can be processed, it is publicly available, stores no user data, and uses state-of-the-art models for its summaries and answers. The result is an application that yields near human-level intuition for answering questions in certain isolated cases, such as Wikipedia and news articles, as well as some scientific texts. The application shows a decrease in reliability and its prediction as to the complexity of the subject, the number of words in the document, and grammatical inconsistency in the questions increases. These are all aspects that can be improved further if used in production.<br>Mängden data som är tillgänglig och konsumeras av människor växer globalt. För att minska den mentala trötthet och öka den allmänna förmågan att få insikt i komplexa, massiva texter eller dokument, har vi utvecklat en applikation för att bistå i de uppgifterna. Applikationen tillåter användare att ladda upp dokument och fråga kontextspecifika frågor via vår webbapplikation. En sammanfattad version av varje dokument presenteras till användaren, vilket kan ytterligare förenkla förståelsen av ett dokument och vägleda dem mot vad som kan vara relevanta frågor att ställa. Vår applikation ger användare möjligheten att behandla olika typer av dokument, är tillgänglig för alla, sparar ingen personlig data, och använder de senaste modellerna inom språkbehandling för dess sammanfattningar och svar. Resultatet är en applikation som når en nära mänsklig intuition för vissa domäner och frågor, som exempelvis Wikipedia- och nyhetsartiklar, samt viss vetensaplig text. Noterade undantag för tillämpningen härrör från ämnets komplexitet, grammatiska korrekthet för frågorna och dokumentets längd. Dessa är områden som kan förbättras ytterligare om den används i produktionen.