@inproceedings{phan-etal-2021-matching,
    title = "Matching The Statements: A Simple and Accurate Model for Key Point Analysis",
    author = "Phan, Viet Hoang  and
      Nguyen, Tien Long  and
      Nguyen, Duc Long  and
      Doan, Ngoc Khanh",
    editor = "Al-Khatib, Khalid  and
      Hou, Yufang  and
      Stede, Manfred",
    booktitle = "Proceedings of the 8th Workshop on Argument Mining",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/sigarab-more-entries-6621/2021.argmining-1.17/",
    doi = "10.18653/v1/2021.argmining-1.17",
    pages = "165--174",
    abstract = "Key Point Analysis (KPA) is one of the most essential tasks in building an Opinion Summarization system, which is capable of generating key points for a collection of arguments toward a particular topic. Furthermore, KPA allows quantifying the coverage of each summary by counting its matched arguments. With the aim of creating high-quality summaries, it is necessary to have an in-depth understanding of each individual argument as well as its universal semantic in a specified context. In this paper, we introduce a promising model, named Matching the Statements (MTS) that incorporates the discussed topic information into arguments/key points comprehension to fully understand their meanings, thus accurately performing ranking and retrieving best-match key points for an input argument. Our approach has achieved the 4th place in Track 1 of the Quantitative Summarization {--} Key Point Analysis Shared Task by IBM, yielding a competitive performance of 0.8956 (3rd) and 0.9632 (7th) strict and relaxed mean Average Precision, respectively."
}