ML Anthology

nBIIG: A Neural BI Insights Generation System for Table Reporting

Yotam Perlitz, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer
AAAI 2023 pp. 16470-16472
doi:10.1609/AAAI.V37I13.27082 /aaai/2023/perlitz2023aaai-nbiig/

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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Cite

Text

Perlitz et al. "nBIIG: A Neural BI Insights Generation System for Table Reporting." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.27082

Markdown

[Perlitz et al. "nBIIG: A Neural BI Insights Generation System for Table Reporting." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/perlitz2023aaai-nbiig/) doi:10.1609/AAAI.V37I13.27082

BibTeX

@inproceedings{perlitz2023aaai-nbiig,
  title     = {{nBIIG: A Neural BI Insights Generation System for Table Reporting}},
  author    = {Perlitz, Yotam and Sheinwald, Dafna and Slonim, Noam and Shmueli-Scheuer, Michal},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16470-16472},
  doi       = {10.1609/AAAI.V37I13.27082},
  url       = {https://mlanthology.org/aaai/2023/perlitz2023aaai-nbiig/}
}