TY  - JOUR
A1  - Kempf, Sebastian
A1  - Krug, Markus
A1  - Puppe, Frank
T1  - KIETA: Key-insight extraction from scientific tables
JF  - Applied Intelligence
N2  - An important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting them and their contained knowledge from scientific publications. The pipeline is split into multiple steps to guarantee modularity as well as analyzability, and agnosticim regarding the specific scientific domain up until the knowledge extraction step, which is based upon an ontology. Additionally, a dataset of corresponding articles has been manually annotated with information regarding table and knowledge extraction. Experiments show promising results that signal the possibility of an automated system, while also indicating limits of extracting knowledge from tables without any context.
KW  - table extraction
KW  - table understanding
KW  - ontology
KW  - key-insight extraction
KW  - information extraction
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324180
SN  - 0924-669X
VL  - 53
IS  - 8
ER  -