Haotong Wang^#,*, Liyan Wang^#, Yves Lepage

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 305-324, 2025, DOI:10.32604/cmc.2025.066351 - 09 June 2025

Abstract Data curation is vital for selecting effective demonstration examples in graph-to-text generation. However, evaluating the quality of Knowledge Graphs (KGs) remains challenging. Prior research exhibits a narrow focus on structural statistics, such as the shortest path length, while the correctness of graphs in representing the associated text is rarely explored. To address this gap, we introduce a dual-perspective evaluation framework for KG-text data, based on the computation of structural adequacy and semantic alignment. From a structural perspective, we propose the Weighted Incremental Edge Method (WIEM) to quantify graph completeness by leveraging agreement between relation models… More >

Tian Zhao^*, Jun Tu^*, Puzheng Quan, Ruisheng Xiong

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1351-1377, 2025, DOI:10.32604/cmc.2025.063209 - 09 June 2025

Abstract Aiming at the problems of incomplete characterization of text relations, poor guidance of potential representations, and low quality of model generation in the field of controllable long text generation, this paper proposes a new GSPT-CVAE model (Graph Structured Processing, Single Vector, and Potential Attention Computing Transformer-Based Conditioned Variational Autoencoder model). The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text, and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation. In the… More >

Haotong Wang^*, Yves Lepage

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4141-4166, 2025, DOI:10.32604/cmc.2025.062806 - 19 May 2025

Abstract Knowledge graphs convey precise semantic information that can be effectively interpreted by neural networks, and generating descriptive text based on these graphs places significant emphasis on content consistency. However, knowledge graphs are inadequate for providing additional linguistic features such as paragraph structure and expressive modes, making it challenging to ensure content coherence in generating text that spans multiple sentences. This lack of coherence can further compromise the overall consistency of the content within a paragraph. In this work, we present the generation of scientific abstracts by leveraging knowledge graphs, with a focus on enhancing both… More >

Jinqiao Dai, Pengsen Cheng, Jiayong Liu^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 547-565, 2024, DOI:10.32604/cmc.2024.051745 - 18 July 2024

Abstract Generating diverse and factual text is challenging and is receiving increasing attention. By sampling from the latent space, variational autoencoder-based models have recently enhanced the diversity of generated text. However, existing research predominantly depends on summarization models to offer paragraph-level semantic information for enhancing factual correctness. The challenge lies in effectively generating factual text using sentence-level variational autoencoder-based models. In this paper, a novel model called fact-aware conditional variational autoencoder is proposed to balance the factual correctness and diversity of generated text. Specifically, our model encodes the input sentences and uses them as facts to… More >

Bowen Sun¹, Yamin Li^1,2,3,*, Jun Zhang¹, Honghong Xu¹, Xiaoqiang Ma⁴, Ping Xia^2,3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 157-176, 2023, DOI:10.32604/cmes.2023.025082 - 05 January 2023

Abstract Generation-based linguistic steganography is a popular research area of information hiding. The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to. However, in the course of our experiment, we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text, which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases, and that the topic of generated texts is uncontrollable, so there is still room for improvement… More >

Cuilin Wang¹, Yuling Liu^1,*, Yongju Tong¹, Jingwen Wang²

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1375-1390, 2021, DOI:10.32604/cmc.2021.017950 - 04 June 2021

Abstract Steganography based on generative adversarial networks (GANs) has become a hot topic among researchers. Due to GANs being unsuitable for text fields with discrete characteristics, researchers have proposed GAN-based steganography methods that are less dependent on text. In this paper, we propose a new method of generative lyrics steganography based on GANs, called GAN-GLS. The proposed method uses the GAN model and the large-scale lyrics corpus to construct and train a lyrics generator. In this method, the GAN uses a previously generated line of a lyric as the input sentence in order to generate the… More >