# Scalable Detection of Salient Entities in News Articles

arXiv: https://arxiv.org/abs/2405.20461 · HTML: https://mlsanity.com/paper/2405.20461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://mlsanity.com/paper/2305.16938/) (2023)
- [Entity-level Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/2102.09130/) (2021)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [Question Answering as an Automatic Evaluation Metric for News Article Summarization](https://mlsanity.com/paper/1906.00318/) (2019)
- [Language Models for Text Classification: Is In-Context Learning Enough?](https://mlsanity.com/paper/2403.17661/) (2024)
- [Document-level Entity-based Extraction as Template Generation](https://mlsanity.com/paper/2109.04901/) (2021)
- [An Entity-Driven Framework for Abstractive Summarization](https://mlsanity.com/paper/1909.02059/) (2019)
- [EntSUM: A Data Set for Entity-Centric Summarization](https://mlsanity.com/paper/2204.02213/) (2022)
- [CitationIE: Leveraging the Citation Graph for Scientific Information Extraction](https://mlsanity.com/paper/2106.01560/) (2021)
- [Capturing Global Informativeness in Open Domain Keyphrase Extraction](https://mlsanity.com/paper/2004.13639/) (2020)
- [Automatic Event Salience Identification](https://mlsanity.com/paper/1809.00647/) (2018)
- [Salience and Market-aware Skill Extraction for Job Targeting](https://mlsanity.com/paper/2005.13094/) (2020)
- [Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling](https://mlsanity.com/paper/1805.01334/) (2018)
- [Extracting Summary Knowledge Graphs from Long Documents](https://mlsanity.com/paper/2009.09162/) (2020)
- [SWAT: A System for Detecting Salient Wikipedia Entities in Texts](https://mlsanity.com/paper/1804.03580/) (2018)
- [Evaluation of Unsupervised Entity and Event Salience Estimation](https://mlsanity.com/paper/2104.06924/) (2021)

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

- [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://mlsanity.com/paper/2504.10792/) (2025)

## Abstract (arXiv)

News articles typically mention numerous entities, a large fraction of which are tangential to the story. Detecting the salience of entities in articles is thus important to applications such as news search, analysis and summarization. In this work, we explore new approaches for efficient and effective salient entity detection by fine-tuning pretrained transformer models with classification heads that use entity tags or contextualized entity representations directly. Experiments show that these straightforward techniques dramatically outperform prior work across datasets with varying sizes and salience definitions. We also study knowledge distillation techniques to effectively reduce the computational cost of these models without affecting their accuracy. Finally, we conduct extensive analyses and ablation experiments to characterize the behavior of the proposed models.