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.- Anthology ID:
- 2024.emnlp-industry.100
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, US
- Editors:
- Franck Dernoncourt, Daniel Preoţiuc-Pietro, Anastasia Shimorina
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1351–1360
- Language:
- URL:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-industry.100/
- DOI:
- 10.18653/v1/2024.emnlp-industry.100
- Cite (ACL):
- Dooyoung Kim, Yoonjin Jang, Dongwook Shin, Chanhoon Park, and Youngjoong Ko. 2024. Hyper-QKSG: Framework for Automating Query Generation and Knowledge-Snippet Extraction from Tables and Lists. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1351–1360, Miami, Florida, US. Association for Computational Linguistics.
- Cite (Informal):
- Hyper-QKSG: Framework for Automating Query Generation and Knowledge-Snippet Extraction from Tables and Lists (Kim et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-industry.100.pdf