Saved You A Click: Automatically Answering Clickbait Titles
This addresses the annoyance of clickbait for online readers, but it is incremental as it applies existing question-answering methods to a new domain.
The paper tackled the problem of clickbait articles by developing a system that automatically extracts answers from website text, so users don't need to read the full articles. They fine-tuned extractive (RoBERTa) and abstractive (T5) models, finding that both improved significantly, with the extractive model slightly better in ROUGE scores and the abstractive one slightly better in BERTscores.
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.