@inproceedings{kim-etal-2024-hyper,
    title = "Hyper-{QKSG}: Framework for Automating Query Generation and Knowledge-Snippet Extraction from Tables and Lists",
    author = "Kim, Dooyoung  and
      Jang, Yoonjin  and
      Shin, Dongwook  and
      Park, Chanhoon  and
      Ko, Youngjoong",
    editor = "Dernoncourt, Franck  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2024",
    address = "Miami, Florida, US",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-industry.100/",
    doi = "10.18653/v1/2024.emnlp-industry.100",
    pages = "1351--1360",
    abstract = "These days, there is an increasing necessity to provide a user with a short knowledge-snippet for a query in commercial information retrieval services such as the featured snippet of Google. In this paper, we focus on how to automatically extract the candidates of query-knowledge snippet pairs from structured HTML documents by using a new Language Model (HTML-PLM). In particular, the proposed system is powerful on extracting them from Tables and Lists, and provides a new framework for automate query generation and knowledge-snippet extraction based on a QA-pair filtering procedure including the snippet refinement and verification processes, which enhance the quality of generated query-knowledge snippet pairs. As a result, 53.8{\%} of the generated knowledge-snippets includes complex HTML structures such as tables and lists in our experiments of a real-world environments, and 66.5{\%} of the knowledge-snippets are evaluated as valid."
}