# Enhancing Keyphrase Generation by BART Finetuning with Splitting and Shuffling

arXiv: https://arxiv.org/abs/2309.06726 · HTML: https://mlsanity.com/paper/2309.06726/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Simple Unsupervised Keyphrase Extraction using Sentence Embeddings](https://mlsanity.com/paper/1801.04470/) (2018)
- [One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases](https://mlsanity.com/paper/1810.05241/) (2018)
- [Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards](https://mlsanity.com/paper/1906.04106/) (2019)
- [One2Set: Generating Diverse Keyphrases as a Set](https://mlsanity.com/paper/2105.11134/) (2021)
- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task](https://mlsanity.com/paper/2304.14177/) (2023)
- [KPDrop: Improving Absent Keyphrase Generation](https://mlsanity.com/paper/2112.01476/) (2021)

## Built on by (most cited first, 3 of 3)

- [EUROPA: A Legal Multilingual Keyphrase Generation Dataset](https://mlsanity.com/paper/2403.00252/) (2024)
- [Pre-Trained Language Models for Keyphrase Prediction: A Review](https://mlsanity.com/paper/2409.01087/) (2024)
- [Exploring Fine-tuned Generative Models for Keyphrase Selection: A Case Study for Russian](https://mlsanity.com/paper/2409.10640/) (2024)

## Abstract (arXiv)

Keyphrase generation is a task of identifying a set of phrases that best repre-sent the main topics or themes of a given text. Keyphrases are dividend int pre-sent and absent keyphrases. Recent approaches utilizing sequence-to-sequence models show effectiveness on absent keyphrase generation. However, the per-formance is still limited due to the hardness of finding absent keyphrases. In this paper, we propose Keyphrase-Focused BART, which exploits the differ-ences between present and absent keyphrase generations, and performs fine-tuning of two separate BART models for present and absent keyphrases. We further show effective approaches of shuffling keyphrases and candidate keyphrase ranking. For absent keyphrases, our Keyphrase-Focused BART achieved new state-of-the-art score on F1@5 in two out of five keyphrase gen-eration benchmark datasets.