# Exclusive Hierarchical Decoding for Deep Keyphrase Generation

arXiv: https://arxiv.org/abs/2004.08511 · HTML: https://mlsanity.com/paper/2004.08511/

## Key facts

- Cited by 38 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 18 Apr 2020.

## Built on by (most cited first, 30 of 38)

- [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)
- [Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training](https://mlsanity.com/paper/2205.10471/) (2022)
- [Is ChatGPT A Good Keyphrase Generator? A Preliminary Study](https://mlsanity.com/paper/2303.13001/) (2023)
- [UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction](https://mlsanity.com/paper/2106.04847/) (2021)
- [Heterogeneous Graph Neural Networks for Keyphrase Generation](https://mlsanity.com/paper/2109.04703/) (2021)
- [Representation Learning for Resource-Constrained Keyphrase Generation](https://mlsanity.com/paper/2203.08118/) (2022)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning](https://mlsanity.com/paper/2104.08799/) (2021)
- [Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation](https://mlsanity.com/paper/2201.05302/) (2022)
- [WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation](https://mlsanity.com/paper/2211.06862/) (2022)
- [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://mlsanity.com/paper/2212.10233/) (2022)
- [Keyphrase Generation Beyond the Boundaries of Title and Abstract](https://mlsanity.com/paper/2112.06776/) (2021)
- [ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task](https://mlsanity.com/paper/2304.14177/) (2023)
- [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://mlsanity.com/paper/2103.12440/) (2021)
- [XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags](https://mlsanity.com/paper/2406.03776/) (2024)
- [Data Augmentation for Low-Resource Keyphrase Generation](https://mlsanity.com/paper/2305.17968/) (2023)
- [Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2310.06374/) (2023)
- [Unsupervised Deep Keyphrase Generation](https://mlsanity.com/paper/2104.08729/) (2021)
- [KPDrop: Improving Absent Keyphrase Generation](https://mlsanity.com/paper/2112.01476/) (2021)
- [SGG: Learning to Select, Guide, and Generate for Keyphrase Generation](https://mlsanity.com/paper/2105.02544/) (2021)
- [KPEval: Towards Fine-Grained Semantic-Based Keyphrase Evaluation](https://mlsanity.com/paper/2303.15422/) (2023)
- [SimCKP: Simple Contrastive Learning of Keyphrase Representations](https://mlsanity.com/paper/2310.08221/) (2023)
- [On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation](https://mlsanity.com/paper/2402.14052/) (2024)
- [One2set + Large Language Model: Best Partners for Keyphrase Generation](https://mlsanity.com/paper/2410.03421/) (2024)
- [Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering](https://mlsanity.com/paper/2309.04734/) (2023)
- [Attend and select: A segment selective transformer for microblog hashtag generation](https://mlsanity.com/paper/2106.03151/) (2021)
- [A Condense-then-Select Strategy for Text Summarization](https://mlsanity.com/paper/2106.10468/) (2021)
- [Deep Keyphrase Completion](https://mlsanity.com/paper/2111.01910/) (2021)
- [Improving Keyphrase Extraction with Data Augmentation and Information Filtering](https://mlsanity.com/paper/2209.04951/) (2022)

## Abstract (arXiv)

Keyphrase generation (KG) aims to summarize the main ideas of a document into a set of keyphrases. A new setting is recently introduced into this problem, in which, given a document, the model needs to predict a set of keyphrases and simultaneously determine the appropriate number of keyphrases to produce. Previous work in this setting employs a sequential decoding process to generate keyphrases. However, such a decoding method ignores the intrinsic hierarchical compositionality existing in the keyphrase set of a document. Moreover, previous work tends to generate duplicated keyphrases, which wastes time and computing resources. To overcome these limitations, we propose an exclusive hierarchical decoding framework that includes a hierarchical decoding process and either a soft or a hard exclusion mechanism. The hierarchical decoding process is to explicitly model the hierarchical compositionality of a keyphrase set. Both the soft and the hard exclusion mechanisms keep track of previously-predicted keyphrases within a window size to enhance the diversity of the generated keyphrases. Extensive experiments on multiple KG benchmark datasets demonstrate the effectiveness of our method to generate less duplicated and more accurate keyphrases.