# Modern Methods for Text Generation

arXiv: https://arxiv.org/abs/2009.04968 · HTML: https://mlsanity.com/paper/2009.04968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [RabindraNet, Creating Literary Works in the Style of Rabindranath Tagore](https://mlsanity.com/paper/2202.00481/) (2022)

## Abstract (arXiv)

Synthetic text generation is challenging and has limited success. Recently, a new architecture, called Transformers, allow machine learning models to understand better sequential data, such as translation or summarization. BERT and GPT-2, using Transformers in their cores, have shown a great performance in tasks such as text classification, translation and NLI tasks. In this article, we analyse both algorithms and compare their output quality in text generation tasks.