English

nBIIG: A Neural BI Insights Generation System for Table Reporting

Computation and Language 2022-11-09 v1

Abstract

We present nBIIG, a neural Business Intelligence (BI) Insights Generation system. Given a table, our system applies various analyses to create corresponding RDF representations, and then uses a neural model to generate fluent textual insights out of these representations. The generated insights can be used by an analyst, via a human-in-the-loop paradigm, to enhance the task of creating compelling table reports. The underlying generative neural model is trained over large and carefully distilled data, curated from multiple BI domains. Thus, the system can generate faithful and fluent insights over open-domain tables, making it practical and useful.

Keywords

Cite

@article{arxiv.2211.04417,
  title  = {nBIIG: A Neural BI Insights Generation System for Table Reporting},
  author = {Yotam Perlitz and Dafna Sheinwald and Noam Slonim and Michal Shmueli-Scheuer},
  journal= {arXiv preprint arXiv:2211.04417},
  year   = {2022}
}

Comments

Accepted to AAAI-23

R2 v1 2026-06-28T05:26:40.453Z