# Clickbait Spoiling via Question Answering and Passage Retrieval

arXiv: https://arxiv.org/abs/2203.10282 · HTML: https://mlsanity.com/paper/2203.10282/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 19 Mar 2022.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://mlsanity.com/paper/2010.06467/) (2020)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [Overview of the TREC 2020 deep learning track](https://mlsanity.com/paper/2102.07662/) (2021)
- [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://mlsanity.com/paper/2006.03236/) (2020)
- [Simple and Effective Multi-Paragraph Reading Comprehension](https://mlsanity.com/paper/1710.10723/) (2017)
- [A Deep Look into Neural Ranking Models for Information Retrieval](https://mlsanity.com/paper/1903.06902/) (2019)
- [Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media](https://mlsanity.com/paper/1610.09786/) (2016)
- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning](https://mlsanity.com/paper/1909.03582/) (2019)
- [Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?](https://mlsanity.com/paper/1703.09400/) (2017)
- [The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength](https://mlsanity.com/paper/1812.10847/) (2018)
- [News Article Teaser Tweets and How to Generate Them](https://mlsanity.com/paper/1807.11535/) (2018)

## Built on by (most cited first, 9 of 9)

- [Prompt-tuning for Clickbait Detection via Text Summarization](https://mlsanity.com/paper/2404.11206/) (2024)
- [What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews](https://mlsanity.com/paper/2601.05563/) (2026)
- [Saved You A Click: Automatically Answering Clickbait Titles](https://mlsanity.com/paper/2212.08196/) (2022)
- [Low-Resource Clickbait Spoiling for Indonesian via Question Answering](https://mlsanity.com/paper/2310.08085/) (2023)
- [Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models](https://mlsanity.com/paper/2602.18171/) (2026)
- [Clickbait Classification and Spoiling Using Natural Language Processing](https://mlsanity.com/paper/2306.14907/) (2023)
- [Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning](https://mlsanity.com/paper/2405.04292/) (2024)
- [Generating clickbait spoilers with an ensemble of large language models](https://mlsanity.com/paper/2405.16284/) (2024)
- [ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models](https://mlsanity.com/paper/2607.20463/) (2026)

## Abstract (arXiv)

We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertises its contents by arousing curiosity instead of providing an informative summary. Our contributions are approaches to classify the type of spoiler needed (i.e., a phrase or a passage), and to generate appropriate spoilers. A large-scale evaluation and error analysis on a new corpus of 5,000 manually spoiled clickbait posts -- the Webis Clickbait Spoiling Corpus 2022 -- shows that our spoiler type classifier achieves an accuracy of 80%, while the question answering model DeBERTa-large outperforms all others in generating spoilers for both types.