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arXiv · 1804.01503

Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings

Paul Azunre·Craig Corcoran·David Sullivan·Garrett Honke·Rebecca Ruppel·Sandeep Verma·Jonathon Morgan
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Abstract

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.

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Paul Azunre, Craig Corcoran, David Sullivan, Garrett Honke, Rebecca Ruppel, Sandeep Verma, Jonathon Morgan. 2018-04-04. Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings. https://arxiv.org/abs/1804.01503

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