Graph-to-Text Generation with Bidirectional Dual Cross-Attention and Concatenation Article Swipe
Elias Lemuye Jimale
,
Wenyu Chen
,
Mugahed A. Al–antari
,
Yeong Hyeon Gu
,
Victor Kwaku Agbesi
,
Wasif Feroze
,
Feidu Akmel
,
Juhar Mohammed Assefa
,
Ali Shahzad
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13060935
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13060935
Graph-to-text generation (G2T) involves converting structured graph data into natural language text, a task made challenging by the need for encoders to capture the entities and their relationships within the graph effectively. While transformer-based encoders have advanced natural language processing, their reliance on linearized data often obscures the complex interrelationships in graph structures, leading to structural loss. Conversely, graph attention networks excel at capturing graph structures but lack the pre-training advantages of transformers. To leverage the strengths of both modalities and bridge this gap, we propose a novel bidirectional dual cross-attention and concatenation (BDCC) mechanism that integrates outputs from a transformer-based encoder and a graph attention encoder. The bidirectional dual cross-attention computes attention scores bidirectionally, allowing graph features to attend to transformer features and vice versa, effectively capturing inter-modal relationships. The concatenation is applied to fuse the attended outputs, enabling robust feature fusion across modalities. We empirically validate BDCC on PathQuestions and WebNLG benchmark datasets, achieving BLEU scores of 67.41% and 66.58% and METEOR scores of 49.63% and 47.44%, respectively. The results outperform the baseline models and demonstrate that BDCC significantly improves G2T tasks by leveraging the synergistic benefits of graph attention and transformer encoders, addressing the limitations of existing approaches and showcasing the potential for future research in this area.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math13060935
- https://www.mdpi.com/2227-7390/13/6/935/pdf?version=1741751815
- OA Status
- gold
- Cited By
- 1
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408307344
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408307344Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math13060935Digital Object Identifier
- Title
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Graph-to-Text Generation with Bidirectional Dual Cross-Attention and ConcatenationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-11Full publication date if available
- Authors
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Elias Lemuye Jimale, Wenyu Chen, Mugahed A. Al–antari, Yeong Hyeon Gu, Victor Kwaku Agbesi, Wasif Feroze, Feidu Akmel, Juhar Mohammed Assefa, Ali ShahzadList of authors in order
- Landing page
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https://doi.org/10.3390/math13060935Publisher landing page
- PDF URL
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https://www.mdpi.com/2227-7390/13/6/935/pdf?version=1741751815Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/13/6/935/pdf?version=1741751815Direct OA link when available
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Concatenation (mathematics), Dual (grammatical number), Computer science, Graph, Theoretical computer science, Combinatorics, Mathematics, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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