Saved You A Click: Automatically Answering Clickbait Titles
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Abstract (arXiv)
Often clickbait articles have a title that is phrased as a question or vague teaser that entices the user to click on the link and read the article to find the explanation. We developed a system that will automatically find the answer or explanation of the clickbait hook from the website text so that the user does not need to read through the text themselves. We fine-tune an extractive question and answering model (RoBERTa) and an abstractive one (T5), using data scraped from the 'StopClickbait' Facebook pages and Reddit's 'SavedYouAClick' subforum. We find that both extractive and abstractive models improve significantly after finetuning. We find that the extractive model performs slightly better according to ROUGE scores, while the abstractive one has a slight edge in terms of BERTscores.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 15 Dec 2022.
Builds on
19 of 29 references.
References · 19
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- 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
- 2019ELI5: Long Form Question Answeringcited by 567
- 2016NewsQA: A Machine Comprehension Datasetcited by 330
7 more
- 2018Bottom-Up Abstractive Summarizationcited by 177
- 2019Unsupervised Question Answering by Cloze Translationcited by 59
- 2017Diversity driven Attention Model for Query-based Abstractive Summarizationcited by 42
- 2020ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generationcited by 39
- 2020Question-Driven Summarization of Answers to Consumer Health Questionscited by 39
- 2022Clickbait Spoiling via Question Answering and Passage Retrievalcited by 9
- 2017Query-Based Abstractive Summarization Using Neural Networkscited by 6
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2 papers.