Graph-Based Extractive Text Summarization Models: A Systematic Review by Abdulkadir Bichi, Pantea Keikhosrokiani, Rohayanti Hassan, Khalil Almekhlafi

“…This paper presents a novel systematic review of various graph-based automatic text summarization models.…”
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Topological Data Analysis and Graph-Based Learning for Multimodal Recommendation by Khalil Bachiri, Ali Yahyaouy, Maria Malek, Nicoleta Rogovschi

“…In this study, we present a new framework that combines Topological Data Analysis (TDA) with graph-based learning to improve multimodal recommendations (TDA-MMRec). …”
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Graph-based adaptive feature fusion neural network model for person-job fit by Xia Xue, Feilong Wang, Jingwen Wang, Bo Ma, Yuyang Yu, Shuling Gao, Jing Chen, Baoli Wang

“…To address this, the present paper proposes a Graph-based Person-Job Fit Neural Network Fusion (GPJFNNF) model. …”
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Improved graph convolutional network for emotion analysis in social media text by Bharti Khemani, Shruti Patil, Sachin Malave, Jaya Gupta

“…This study presents an Improved Graph Convolutional Network (IGCN) that leverages the network structure of social media text to enhance emotion classification. …”
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Rumor detection using dual embeddings and text-based graph convolutional network by Barsha Pattanaik, Sourav Mandal, Rudra M. Tripathy, Arif Ahmed Sekh

“…Their success is due to their ability to identify structural patterns in rumors and effectively use neighborhood information. We present a novel rumor detection model using TextGCN, which utilizes a word-document graph to represent rumor texts. …”
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Financial risk forecasting with RGCT-prerisk: a relational graph and cross-temporal contrastive pretraining framework by Liyu Chen, Xiangwei Fan

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Unsupervised Chunking Based on Graph Propagation from Bilingual Corpus by Ling Zhu, Derek F. Wong, Lidia S. Chao

“…This paper presents a novel approach for unsupervised shallow parsing model trained on the unannotated Chinese text of parallel Chinese-English corpus. …”
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An intelligent guided troubleshooting method for aircraft based on HybirdRAG by Xiaoyue Xie, Xilang Tang, Siwei Gu, Lijie Cui

“…Unlike conventional retrieval-augmented generation (RAG) methods that rely on single-modal retrieval, HybridRAG adopts a multi-dimensional retrieval strategy, combining graph-based reasoning with both vector-based and BM25-based text retrieval techniques. …”
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Advancing Hate Speech Detection in Indonesian Language Using Graph Neural Networks and TF-IDF by Syaikha Amirah Zikrina, Fitriyani

“…This study highlights the advantage of graph-based approaches in capturing complex relationships within text data. …”
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Knowledge Graph-Driven Approach in Aspect-Based Sentiment Analysis: Exploring the Impact of Embedding Techniques by Souha Al Katat, Chamseddine Zaki, Hussein Hazimeh, Ibrahim El Bitar, Rafael Angarita, Lionel Trojman

“…Despite the high performance of the existing embedding approaches for Aspect-Based Sentiment Analysis (ABSA), such as Word2Vec and GloVe, they still have several limitations, mainly in contextual understanding and relational insights of natural language, especially in complex and long sentences. This paper presents a novel approach that enhances ABSA by integrating knowledge graphs into a transformer model, where the graphs are automatically built from raw text without requiring external resources, making the system adaptable and fully data-driven. …”
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User preference modeling for movie recommendations based on deep learning by Yang Gao, Hong Zheng, Haonan Cui

“…In this study, we employ Artificial Intelligence (AI), graph-based techniques, and text mining to accurately estimate user preferences. …”
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MMEmAsis: multimodal emotion and sentiment analysis by Gleb A. Kiselev, Yaroslava M. Lubysheva, Daniil A. Weizenfeld

“…The paper presents a new multimodal approach to analyzing the psycho-emotional state of a person using nonlinear classifiers. …”
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Performance of an End-to-End Inventory Demand Forecasting Pipeline Using a Federated Data Ecosystem by Henrique Duarte Moura, Els de Vleeschauwer, Gerald Haesendonck, Ben De Meester, Lynn D’eer, Tom De Schepper, Siegfried Mercelis, Erik Mannens

“…Graph deep learning forecasting has the ability to comprehend intricate relationships, and it seamlessly tunes into the diverse, multi-retailer data present in a federated setup. The system aims to create a unified data view without centralization, addressing technical and operational challenges, which are discussed throughout the text. …”
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