I am currently a 3rd-year Ph.D. student in Computer Science department at the University of Central Florida, advised by Dr. Fei Liu. I'm a member of the UCF NLP Group.

I was an undergraduate student researcher at Fudan University during 2012-16, working with Dr. Wei Zhang and Dr. Xiangyang Xue.

Research Interests

My research focuses on developing automatic summarization systems to generate concise and informative summaries from a large collection of documents to support fast browsing of textual content. My current research work combines natural language processing with cutting-edge deep neural models. I'm also interested in reinforcement learning and AI-related topics.

  • Text Generation and Summarization
  • Language Comprehension and Grammar
  • Deep Neural Networks
  • Reinforcement Learning and other AI-related topics


  • Scoring Sentence Singletons and Pairs for Abstractive Summarization
    Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, and Fei Liu
    Accepted at the 57th Annual Meeting of the Association for Computational Linguistics (ACL) , Florence, France, July 2019. [paper][code]
  • Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
    Logan Lebanoff, Kaiqiang Song, and Fei Liu
    Accepted at the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Brussels, Belgium, November 2018. [paper][code]
  • Structure-Infused Copy Mechanisms for Abstractive Summarization
    Kaiqiang Song, Lin Zhao, and Fei Liu
    Accepted at the 27th International Conference on Computational Linguistics (COLING), Santa Fe, New-Mexico, August 2018. [paper][code]
    (Oral Presentation)


Structure-Infused Copy Mechanisms for Abstractive Summarization

The seq2seq paradigm has achieved remarkable success in summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of the source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important source words and relations to summaries. The approach naturally combines the dependency structure of source sentences with the copy mechanism of an abstractive summarization framework. It outperforms state-of-the-art systems on the benchmark summarization dataset. Experimental results also demonstrate the effectiveness of the approach at preserving salient source words and dependency relations.


  • Address

    Kaiqiang Song
    Department of Computer Science, HEC 234
    University of Central Florida
    4000 Central Florida Blvd
    Orlando, FL 32816
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