Towards Automated Survey Variable Search and Summarization in Social Science Publications

作     者：Kartal, Yavuz Selim Takeshita, Sotaro Tsereteli, Tornike Eckert, Kai Kroll, Henning Mayr, Philipp Ponzetto, Simone Paolo Zapilko, Benjamin Zielinski, Andrea 

作者机构：GESIS Leibniz Institute for the Social Sciences Germany Data and Web Science Group University of Mannheim Germany Fraunhofer Institute for Systems and Innovation Research ISI Germany Web-based Information Systems and Services Stuttgart Media University Germany 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Surveys 

摘      要：Nowadays there is a growing trend in many scientific disciplines to support researchers by providing enhanced information access through linking of publications and underlying datasets, so as to support research with infrastructure to enhance reproducibility and reusability of research results. In this research note, we present an overview of an ongoing research project, named VADIS (VAriable Detection, Interlinking and Summarization), that aims at developing technology and infrastructure for enhanced information access in the Social Sciences via search and summarization of publications on the basis of automatic identification and indexing of survey variables in text. We provide an overview of the overarching vision underlying our project, its main components, and related challenges, as well as a thorough discussion of how these are meant to address the limitations of current information access systems for publications in the Social Sciences. We show how this goal can be concretely implemented in an end-user system by presenting a search prototype, which is based on user requirements collected from qualitative interviews with empirical Social Science researchers. Copyright © 2022, The Authors. All rights reserved.