nBIIG: A Neural BI Insights Generation System for Table Reporting
November 08, 2022 ยท Declared Dead ยท ๐ AAAI Conference on Artificial Intelligence
"No code URL or promise found in abstract"
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Authors
Yotam Perlitz, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer
arXiv ID
2211.04417
Category
cs.CL: Computation & Language
Citations
2
Venue
AAAI Conference on Artificial Intelligence
Last Checked
5 months ago
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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