Text Assisted Insight Ranking Using Context-Aware Memory Network

Qi Zeng*, Liangchen Luo*, Wenhao Huang, Yang Tang.
In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii. 2019.

Abstract

Extracting valuable facts or informative summaries from multi-dimensional tables, i.e. insight mining, is an important task in data analysis and business intelligence. However, ranking the importance of insights remains a challenging and unexplored task. The main challenge is that explicitly scoring an insight or giving it a rank requires a thorough understanding of the tables and costs a lot of manual efforts, which leads to the lack of available training data for the insight ranking problem. In this paper, we propose an insight ranking model that consists of two parts: A neural ranking model explores the data characteristics, such as the header semantics and the data statistical features, and a memory network model introduces table structure and context information into the ranking process. We also build a dataset with text assistance. Experimental results show that our approach largely improves the ranking precision as reported in multi evaluation metrics.

BibTex

@inproceedings{Zeng2019InsightRanking,
author = {Zeng, Qi and Luo, Liangchen and Huang, Wenhao and Tang, Yang},
title = {Text Assisted Insight Ranking Using Context-Aware Memory Network},
booktitle = {Proceedings of the 33rd AAAI Conference on Artificial Intelligence},
month = {January},
year = {2019},