Unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering

Hajjar, Ali; Tekli, Joe

doi:10.1007/978-3-031-15743-1_23

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…In recent years, we have witnessed the rapid advancement of deep learning and its successful applications (Sarivougioukas & Vagelatos, 2022;Lv et al, 2022;Zhou et al, 2022b;Jiao et al, 2023). NLP is a pivotal domain within deep learning and encompasses various developmental trajectories and applications (Ismail et al, 2022;Vats et al, 2023), such as text classification (Singh & Sachan, 2021;Miri et al, 2022), text-to-image synthesis (Chopra et al, 2022), and unsupervised information extraction (Sarkissian & Tekli, 2021;Hajjar & Tekli, 2022). Among these domains, knowledge graphs are an indispensable component and have extensive applications in various fields (Zhao et al, 2022;Zhou et al, 2022a;Li et al, 2023), such as health care and cybersecurity (Gou et al, 2017;Sahoo & Gupta, 2019).…”

Section: Related Workmentioning

confidence: 99%

A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling

Zhang,

et al. 2023

International Journal on Semantic Web and Information Systems

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The NER task is largely developed based on well-annotated data. However, in many scenarios, the entities may not be fully annotated, leading to serious performance degradation. To address this issue, the authors propose a robust NER approach that combines a novel PU-learning algorithm and negative sampling. Unlike many existing studies, the proposed method adopts a two-step procedure for handling unlabeled entities, thereby enhancing its capability to mitigate the impact of such entities. Moreover, this algorithm demonstrates high versatility and can be integrated into any token-level NER model with ease. The effectiveness of the proposed method is verified on several classic NER models and datasets, demonstrating its strong ability to handle unlabeled entities. Finally, the authors achieve competitive performances on synthetic and real-world datasets.

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Section: Related Workmentioning

confidence: 99%

A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling

Zhang,

et al. 2023

International Journal on Semantic Web and Information Systems

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“…Text Sentiment Analysis, also referred to as Opinion Mining, is a process aided by computers to swiftly capture and organize the subjective evaluation information on the internet. It adeptly mines and assesses individuals' viewpoints, emotions, reviews, stances, and the sentimental inclinations of texts about entities like products, services, organizations, events, and topics, followed by inductive reasoning and inference of this mined data (Do et al, 2022;Hajjar & Tekli, 2022;Wang & Yang, 2021).…”

Section: Introductionmentioning

confidence: 99%

Short Text Semantic Sentiment Analysis Based on Dual Channel Aspect Attention in Intelligent Systems

2024

International Journal on Semantic Web and Information Systems

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Traditional deep learning models for text sentiment analysis fail to fully harness the contextual semantic information of aspect nodes or use prior sentiment resources. This paper proposes a dual channel sentiment analysis model named M2BERT-BLSTM AA that is based on an enhanced Bidirectional Encoder Representations from Transformers(BERT)and Bidirectional Long short-term memory(BLSTM) model and incorporates a Dual Attention Mechanism. Firstly, an emotional resource database is constructed using existing emotional resources. Secondly, vectors are concatenated following mean and max pooling along the dimension of sentence length. These semantic features mitigate evaluation imbalance.Then the text and sentiment information are encoded separately, using distinct Attention Mechanism(Att-M) to extract contextual relationships and emotional features. The model's Aspect-Based multi-class sentiment prediction accuracies on the three Chinese datasets of Meituan ordering, restaurants, and laptops are 75.2%, 87.5%, and 75%, respectively, showing improved performance on sentiment classification.

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“…The ability to make good choices at the right moment is becoming more and more crucial in today's world. Smarter, more timely judgments may be made with the help of cutting-edge technology like artificial intelligence (AI) (Hajjar & Tekli, 2022;Dhaini & Mansour, 2021;Xu et al, 2021;Deveci et al, 2022), machine learning (ML) (Tay & Mourad, 2020;Abbas et al, 2021;Gegres et al, 2022;Maroun et al, 2022), and big data (Slim et al, 2021;Wu et al, 2016;Kaur et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Role of Global Information Management in Advanced Decision Support Systems (DSS) for Sustainable Development

Gupta

Panigrahi

2023

Journal of Global Information Management

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Timely intelligent decision support systems (DSS) are increasingly important for the sustainable development of entrepreneurship. Global information management plays an important role in accurate DSS. Judgments can be made more quickly, accurately, and objectively thanks to the availability of large data and sophisticated artificial intelligence in the realm of quantitative smart decisions. In this context, this research analyzes the contribution of global information management for sustainable business development through DSS. This paper used the Scopus database to collect relevant research papers related to the research topic. This research helps researchers analyze the recent trend and development in the field of DSS in the context of global information management.

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Unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering

Cited by 4 publications

References 23 publications

A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling

A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling

Short Text Semantic Sentiment Analysis Based on Dual Channel Aspect Attention in Intelligent Systems

Analysis of the Role of Global Information Management in Advanced Decision Support Systems (DSS) for Sustainable Development

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