# Capturing Global Informativeness in Open Domain Keyphrase Extraction

arXiv: https://arxiv.org/abs/2004.13639 · HTML: https://mlsanity.com/paper/2004.13639/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [Is ChatGPT A Good Keyphrase Generator? A Preliminary Study](https://mlsanity.com/paper/2303.13001/) (2023)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation](https://mlsanity.com/paper/2208.09606/) (2022)
- [SimCKP: Simple Contrastive Learning of Keyphrase Representations](https://mlsanity.com/paper/2310.08221/) (2023)
- [Large Language Models as Zero-Shot Keyphrase Extractors: A Preliminary Empirical Study](https://mlsanity.com/paper/2312.15156/) (2023)
- [Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation](https://mlsanity.com/paper/2303.08318/) (2023)
- [A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2405.16571/) (2024)
- [Pre-Trained Language Models for Keyphrase Prediction: A Review](https://mlsanity.com/paper/2409.01087/) (2024)
- [Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction](https://mlsanity.com/paper/2308.08739/) (2023)
- [Detecting Relevant Information in High-Volume Chat Logs: Keyphrase Extraction for Grooming and Drug Dealing Forensic Analysis](https://mlsanity.com/paper/2311.04905/) (2023)
- [Scalable Detection of Salient Entities in News Articles](https://mlsanity.com/paper/2405.20461/) (2024)
- [LongKey: Keyphrase Extraction for Long Documents](https://mlsanity.com/paper/2411.17863/) (2024)
- [Multi-Document Keyphrase Extraction: Dataset, Baselines and Review](https://mlsanity.com/paper/2110.01073/) (2021)
- [Cross-Domain Keyword Extraction with Keyness Patterns](https://mlsanity.com/paper/2409.18724/) (2024)
- [SEKE: Specialised Experts for Keyword Extraction](https://mlsanity.com/paper/2412.14087/) (2024)
- [A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization](https://mlsanity.com/paper/2604.01452/) (2026)
- [Building a Multimodal Dataset of Academic Paper for Keyword Extraction](https://mlsanity.com/paper/2606.31069/) (2026)

## Abstract (arXiv)

Open-domain KeyPhrase Extraction (KPE) aims to extract keyphrases from documents without domain or quality restrictions, e.g., web pages with variant domains and qualities. Recently, neural methods have shown promising results in many KPE tasks due to their powerful capacity for modeling contextual semantics of the given documents. However, we empirically show that most neural KPE methods prefer to extract keyphrases with good phraseness, such as short and entity-style n-grams, instead of globally informative keyphrases from open-domain documents. This paper presents JointKPE, an open-domain KPE architecture built on pre-trained language models, which can capture both local phraseness and global informativeness when extracting keyphrases. JointKPE learns to rank keyphrases by estimating their informativeness in the entire document and is jointly trained on the keyphrase chunking task to guarantee the phraseness of keyphrase candidates. Experiments on two large KPE datasets with diverse domains, OpenKP and KP20k, demonstrate the effectiveness of JointKPE on different pre-trained variants in open-domain scenarios. Further analyses reveal the significant advantages of JointKPE in predicting long and non-entity keyphrases, which are challenging for previous neural KPE methods. Our code is publicly available at https://github.com/thunlp/BERT-KPE.