nBIIG: A Neural BI Insights Generation System for Table Reporting

November 08, 2022 ยท Declared Dead ยท ๐Ÿ› AAAI Conference on Artificial Intelligence

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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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