Hyper- QKSG : Framework for Automating Query Generation and Knowledge-Snippet Extraction from Tables and Lists (2024)

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.

## introduction the continued expansion of the internet landscape and the explosion of digital content have led to a tremendous increase in the amount of web data, including news, blogs, forums, social media, and other websites. in the vast ocean of information, users are demanding effective and expedient methods of filtering and accessing information, which can return the information that meets the user's needs. in this context, knowledge-snippet ("featured snippets" in google), a compressed excerpt that contains the answer to a user query, are playing an important role in helping users obtain the information they require with greater speed and ease. users should be able to get enough information relevant to their query from the knowledge snippet, and knowledge snippets should be able to be extracted from different document structures, such as lists and tables, as needed. ## methodology figure 2 provides the overview of hyper-qksg. the overall framework consists of the following three stages: 1) generating query-answer (qa) pairs to extract knowledge-snippets, 2) improving qa pairs with refinement, and 3) extracting knowledge-snippets. for the first stage, the htmlanswer extraction model and the query generation model are used to extract answer candidates and generate their corresponding queries, respectively. in the second stage, the answer candidate and query pairs are refined sequentially. in the third step, the html-snippet extraction model is developed to extract knowledge-snippets. section 3.1 introduces the details of the overall framework, section 3.2 explains how to pretrain the html-plm model, which is the backbone model of the html-answer and html-snippet extraction models, and section 3.3 discusses the finetuning of each pipeline system in the overall framework. ## conclusion in this paper, we present the query-knowledge snippet extraction framework, the hyper-qksg framework, for the effective web search. to develop this framework, we propose html-plm, which pretrained html-based language models for information extraction from diverse html structures, and it can significantly enhance the performance of html-based mrc downstream tasks in our experiments. in addition, we analyze the knowledge-snippets generated by the framework and find that the proportion of knowledge-snippets with table and list structures is very large in realworld data. therefore, the proposed html-plm is actively utilized in the knowledge-snippet extraction as more important module. to improve query and knowledge-snippet quality, we propose various filtering, refinement, and verification methods. these are proven as effective methods through the ablation study.