nBIIG: A Neural BI Insights Generation System for Table Reporting
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 8 Nov 2022.
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References · 5
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 711
- 2020Logic2Text: High-Fidelity Natural Language Generation from Logical Formscited by 45
- 2020Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inferencecited by 43
- 2019EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inferencecited by 39
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Citing papers per year: 2024: 1.
1 paper.
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