SemSUM: Semantic Dependency Guided Neural Abstractive Summarization


  • Hanqi Jin Peking University
  • Tianming Wang Peking University
  • Xiaojun Wan Peking University



In neural abstractive summarization, the generated summaries often face semantic irrelevance and content deviation from the input sentences. In this work, we incorporate semantic dependency graphs about predicate-argument structure of input sentences into neural abstractive summarization for the problem. We propose a novel semantics dependency guided summarization model (SemSUM), which can leverage the information of original input texts and the corresponding semantic dependency graphs in a complementary way to guide summarization process. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. Experiments show that the proposed model improves semantic relevance and reduces content deviation, and also brings significant improvements on automatic evaluation ROUGE metrics.




How to Cite

Jin, H., Wang, T., & Wan, X. (2020). SemSUM: Semantic Dependency Guided Neural Abstractive Summarization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8026-8033.



AAAI Technical Track: Natural Language Processing