# End-to-end Learning for Short Text Expansion

arXiv: https://arxiv.org/abs/1709.00389 · HTML: https://mlsanity.com/paper/1709.00389/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [A Benchmark for Text Expansion: Datasets, Metrics, and Baselines](https://mlsanity.com/paper/2309.09198/) (2023)

## Abstract (arXiv)

Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to first expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data sets.