# PSG: Prompt-based Sequence Generation for Acronym Extraction

arXiv: https://arxiv.org/abs/2111.14301 · HTML: https://mlsanity.com/paper/2111.14301/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [How Language-Neutral is Multilingual BERT?](https://mlsanity.com/paper/1911.03310/) (2019)
- [A Survey of Deep Learning Techniques for Neural Machine Translation](https://mlsanity.com/paper/2002.07526/) (2020)
- [More but Correct: Generating Diversified and Entity-revised Medical Response](https://mlsanity.com/paper/2108.01266/) (2021)
- [Empower Sequence Labeling with Task-Aware Neural Language Model](https://mlsanity.com/paper/1709.04109/) (2017)
- [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2010.14678/) (2020)
- [Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation](https://mlsanity.com/paper/2109.06513/) (2021)
- [Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding](https://mlsanity.com/paper/2012.11760/) (2020)
- [A Survey on Neural Machine Reading Comprehension](https://mlsanity.com/paper/1906.03824/) (2019)

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

- [A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick](https://mlsanity.com/paper/2406.15352/) (2024)
- [Capability Localization: Capabilities Can be Localized rather than Individual Knowledge](https://mlsanity.com/paper/2502.20992/) (2025)

## Abstract (arXiv)

Acronym extraction aims to find acronyms (i.e., short-forms) and their meanings (i.e., long-forms) from the documents, which is important for scientific document understanding (SDU@AAAI-22) tasks. Previous works are devoted to modeling this task as a paragraph-level sequence labeling problem. However, it lacks the effective use of the external knowledge, especially when the datasets are in a low-resource setting. Recently, the prompt-based method with the vast pre-trained language model can significantly enhance the performance of the low-resourced downstream tasks. In this paper, we propose a Prompt-based Sequence Generation (PSG) method for the acronym extraction task. Specifically, we design a template for prompting the extracted acronym texts with auto-regression. A position extraction algorithm is designed for extracting the position of the generated answers. The results on the acronym extraction of Vietnamese and Persian in a low-resource setting show that the proposed method outperforms all other competitive state-of-the-art (SOTA) methods.