# Deep Keyphrase Completion

arXiv: https://arxiv.org/abs/2111.01910 · HTML: https://mlsanity.com/paper/2111.01910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [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)
- [Exclusive Hierarchical Decoding for Deep Keyphrase Generation](https://mlsanity.com/paper/2004.08511/) (2020)
- [Semi-Supervised Learning for Neural Keyphrase Generation](https://mlsanity.com/paper/1808.06773/) (2018)
- [Title-Guided Encoding for Keyphrase Generation](https://mlsanity.com/paper/1808.08575/) (2018)
- [Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention](https://mlsanity.com/paper/2008.01739/) (2020)
- [Keyphrase Generation for Scientific Document Retrieval](https://mlsanity.com/paper/2106.14726/) (2021)
- [DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases](https://mlsanity.com/paper/1905.07689/) (2019)
- [Keyphrase Prediction With Pre-trained Language Model](https://mlsanity.com/paper/2004.10462/) (2020)
- [Keyphrase Generation: A Text Summarization Struggle](https://mlsanity.com/paper/1904.00110/) (2019)
- [Keyphrase Extraction from Disaster-related Tweets](https://mlsanity.com/paper/1910.07897/) (2019)
- [Keyphrase Generation for Scientific Articles using GANs](https://mlsanity.com/paper/1909.12229/) (2019)

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

- [MetaKP: On-Demand Keyphrase Generation](https://mlsanity.com/paper/2407.00191/) (2024)

## Abstract (arXiv)

Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval. Though previous studies have made substantial efforts for automated keyphrase extraction and generation, surprisingly, few studies have been made for textitkeyphrase completion (KPC). KPC aims to generate more keyphrases for document (e.g. scientific publication) taking advantage of document content along with a very limited number of known keyphrases, which can be applied to improve text indexing system, etc. In this paper, we propose a novel KPC method with an encoder-decoder framework. We name it textitdeep keyphrase completion (DKPC) since it attempts to capture the deep semantic meaning of the document content together with known keyphrases via a deep learning framework. Specifically, the encoder and the decoder in DKPC play different roles to make full use of the known keyphrases. The former considers the keyphrase-guiding factors, which aggregates information of known keyphrases into context. On the contrary, the latter considers the keyphrase-inhibited factor to inhibit semantically repeated keyphrase generation. Extensive experiments on benchmark datasets demonstrate the efficacy of our proposed model.