This paper describes an abstractive summarization method for tabular data which employs a knowledge base semantic embedding to generate the summary. Assuming the dataset contains descriptive text in headers, columns and/or some augmenting metadata, the system employs the embedding to recommend a subject/type for each text segment. Recommendations are aggregated into a small collection of super types considered to be descriptive of the dataset by exploiting the hierarchy of types in a pre-specified ontology. Using February 2015 Wikipedia as the knowledge base, and a corresponding DBpedia ontology as types, we present experimental results on open data taken from several sources--OpenML, CKAN and data.world--to illustrate the effectiveness of the approach.
@article{arxiv.1804.01503,
title = {Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings},
author = {Paul Azunre and Craig Corcoran and David Sullivan and Garrett Honke and Rebecca Ruppel and Sandeep Verma and Jonathon Morgan},
journal= {arXiv preprint arXiv:1804.01503},
year = {2018}
}