Literature Review of Automatic Text Summarization: Research Trend, Dataset and Method

Widyassari, Adhika Pramita; Affandy, Affandy; Noersasongko, Edy; Fanani, Ahmad Zainul; Syukur, Abdul; Basuki, Ruri Suko

doi:10.1109/icoiact46704.2019.8938454

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…The paper identifies a shift in the text summarization research trend towards optimization in the last three years, emphasizing efforts to enhance performance and achieve high accuracy in summarization processes. [5] In their collaborative work presented at the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) in Erode, India, Rahul, S. Adhikari, and Monika delve into the significance of text summarization in the current data-rich environment. The paper emphasizes the crucial role of summarization in distilling the right amount of information from extensive texts, such as lengthy articles on news websites, blogs, and customer review platforms.…”

Section: Literature Reviewmentioning

confidence: 99%

News Summarization of BBC Articles: A Multi-Category Approach

Kevin

2024

IJSREM

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In this research project, we explore the application of advanced natural language processing techniques to automatically summarize news articles from the BBC. The dataset comprises five distinct categories— business, entertainment, politics, sport, and tech—each containing a wealth of information. Our primary goal is to develop an efficient and accurate news summarization system using state-of-the-art language models. We employ the Hugging Face Transformers library to create a summarization pipeline capable of extracting key information from lengthy news articles.

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Section: Literature Reviewmentioning

confidence: 99%

News Summarization of BBC Articles: A Multi-Category Approach

Kevin

2024

IJSREM

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“…The study selection is a critical step in the systematic literature review process [34]. It involves reviewing the titles and abstracts of the articles obtained through the search strategy to identify relevant studies that meet the inclusion and exclusion criteria.…”

Section: Study Selectionmentioning

confidence: 99%

Exploring the Landscape of Intrinsic Plagiarism Detection: Benchmarks, Techniques, Evolution, and Challenges

Manzoor,

Farooq,

Haseeb

et al. 2023

IEEE Access

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Intrinsic plagiarism detection is a critical domain in the field of text analysis that aims to identify plagiarized content within a document to check whether parts of the document are from the same author. As the development of Large Language Models (LLMs) based content generation tools such as, ChatGPT is publicly available, the challenge of intrinsic plagiarism has become increasingly significant in various domains. Consequently, there is a growing demand for robust and accurate detection methods to address this evolving landscape. In this context, the present study conducts a comprehensive Systematic Literature Review (SLR) to explore the landscape of intrinsic plagiarism detection. We systematically collected and rigorously analyzed 44 research papers that delve into various aspects of the domain, including common datasets, feature extraction techniques and intrinsic plagiarism detection methods. This SLR also highlights the evolution of detection approaches over time and the challenges faced in this context especially challenges associated with low-resource languages. To the best of our knowledge, there is no SLR exclusively based on the intrinsic plagiarism detection that bridge the gap in existing literature and offering valuable insights to researchers and practitioners. By consolidating the state-of-the-art findings, this SLR serves as a foundation for future research, enabling the development of more effective and efficient plagiarism detection solutions to combat the ever-evolving challenges posed by plagiarism in today's digital age.

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“…When the agent picks the candidate sentence as part of the summary, the output Q-value reflects the expected value. [305], [306], [307] proposed a Q-network for text summarization.…”

Section: ) Deep Learning Algorithmmentioning

confidence: 99%