nBIIG: A Neural BI Insights Generation System for Table Reporting

Authors

  • Yotam Perlitz IBM Research
  • Dafna Sheinwald IBM Research
  • Noam Slonim IBM Research
  • Michal Shmueli-Scheuer IBM Research

DOI:

https://doi.org/10.1609/aaai.v37i13.27082

Keywords:

Insights Generation, Neural Models, Business Intelligence, Artificial Intelligence

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.

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Published

2024-07-15

How to Cite

Perlitz, Y., Sheinwald, D., Slonim, N., & Shmueli-Scheuer, M. (2024). nBIIG: A Neural BI Insights Generation System for Table Reporting. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16470-16472. https://doi.org/10.1609/aaai.v37i13.27082