Abstract
Cross-Lingual Summarization (CLS) is the task of generating summaries in a target language for source articles in a different language. Previous studies on CLS mainly take pipeline methods or train an attention-based end-to-end model on translated parallel datasets. However, challenges arising from lengthy sources and non-parallel mappings hamper the accurate summarization and translation of pivotal information. To address this, this paper proposes a novel Dual Kknowledge-enhanced abstractive CLS model (DKCS) via a graph-encoder-decoder architecture. DKCS implements a clue-focused graph encoder that utilizes a graph attention network to simultaneously capture inter-sentence structures and significant information guided by extracted salient internal knowledge. Additionally, a bilingual lexicon is introduced in the decoder with an attention layer for enhanced translation. We construct the first hand-written CLS dataset for evaluation as well. Experimental results demonstrate the model’s robustness and significant performance gains over the existing SOTA on both automatic and human evaluations.
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Notes
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CyEn2ZhSum dataset is available in https://github.com/MollyShuu/CyEn2ZhSum.
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Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (No. U19A2081), the National Key Research and Development Program of China (No. 2022YFC3303101), the Key Research and Development Program of Science and Technology Department of Sichuan Province(No. 2023YFG0145), Science and Engineering Connotation Development Project of Sichuan University (No. 2020SCUNG129) and the Fundamental Research Funds for the Central Universities (No. 2023SCU12126).
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Jiang, S., Tu, D., Chen, X., Tang, R., Wang, W., Wang, H. (2024). DKCS: A Dual Knowledge-Enhanced Abstractive Cross-Lingual Summarization Method Based on Graph Attention Networks. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1967. Springer, Singapore. https://doi.org/10.1007/978-981-99-8178-6_9
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