# Importance Estimation from Multiple Perspectives for Keyphrase Extraction

arXiv: https://arxiv.org/abs/2110.09749 · HTML: https://mlsanity.com/paper/2110.09749/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)
- [Topic-Aware Neural Keyphrase Generation for Social Media Language](https://mlsanity.com/paper/1906.03889/) (2019)
- [Open Domain Web Keyphrase Extraction Beyond Language Modeling](https://mlsanity.com/paper/1911.02671/) (2019)
- [DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases](https://mlsanity.com/paper/1905.07689/) (2019)
- [Keyphrase Extraction with Span-based Feature Representations](https://mlsanity.com/paper/2002.05407/) (2020)
- [Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference](https://mlsanity.com/paper/2010.12828/) (2020)

## Built on by (most cited first, 16 of 16)

- [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)
- [Hyperbolic Relevance Matching for Neural Keyphrase Extraction](https://mlsanity.com/paper/2205.02047/) (2022)
- [KPDrop: Improving Absent Keyphrase Generation](https://mlsanity.com/paper/2112.01476/) (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)
- [Large Language Models as Zero-Shot Keyphrase Extractors: A Preliminary Empirical Study](https://mlsanity.com/paper/2312.15156/) (2023)
- [On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation](https://mlsanity.com/paper/2402.14052/) (2024)
- [A Preliminary Empirical Study on Prompt-based Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2405.16571/) (2024)
- [One2set + Large Language Model: Best Partners for Keyphrase Generation](https://mlsanity.com/paper/2410.03421/) (2024)
- [Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction](https://mlsanity.com/paper/2308.08739/) (2023)
- [Enhancing Keyphrase Extraction from Academic Articles Using Section Structure Information](https://mlsanity.com/paper/2505.14149/) (2025)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2409.10907/) (2024)
- [Cross-Domain Keyword Extraction with Keyness Patterns](https://mlsanity.com/paper/2409.18724/) (2024)
- [Implicit Manipulation for Skill Selection in LLM Agents with Semantic Matching](https://mlsanity.com/paper/2609.02035/) (2026)

## Abstract (arXiv)

Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estimation. From the view of human understanding documents, we typically measure the importance of phrase according to its syntactic accuracy, information saliency, and concept consistency simultaneously. However, most existing keyphrase extraction approaches only focus on the part of them, which leads to biased results. In this paper, we propose a new approach to estimate the importance of keyphrase from multiple perspectives (called as textitKIEMP) and further improve the performance of keyphrase extraction. Specifically, textitKIEMP estimates the importance of phrase with three modules: a chunking module to measure its syntactic accuracy, a ranking module to check its information saliency, and a matching module to judge the concept (i.e., topic) consistency between phrase and the whole document. These three modules are seamlessly jointed together via an end-to-end multi-task learning model, which is helpful for three parts to enhance each other and balance the effects of three perspectives. Experimental results on six benchmark datasets show that textitKIEMP outperforms the existing state-of-the-art keyphrase extraction approaches in most cases.