Hyper-QKSG: Framework for Automating Query Generation and Knowledge-Snippet Extraction from Tables and Lists
Dooyoung Kim, Yoonjin Jang, Dongwook Shin, Chanhoon Park, Youngjoong Ko
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.
BibTeX
@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://aclanthology.org/2024.emnlp-industry.100/",
doi = "10.18653/v1/2024.emnlp-industry.100",
pages = "1351--1360"
}