Prompt-tuning for Clickbait Detection via Text Summarization
Haoxiang Deng Yi Zhu Ye Wang Jipeng Qiang Yunhao Yuan Yun Li Runmei Zhang
Clickbaits are surprising social posts or deceptive news headlines that
attempt to lure users for more clicks, which have posted at unprecedented rates
for more profit or commercial revenue. The spread of clickbait has significant
negative impacts on the users, which brings users misleading or even
click-jacking attacks. Different from fake news, the crucial problem in
clickbait detection is determining whether the headline matches the
corresponding content. Most existing methods compute the semantic similarity
between the headlines and contents for detecting clickbait. However, due to
significant differences in length and semantic features between headlines and
contents, directly calculating semantic similarity is often difficult to
summarize the relationship between them. To address this problem, we propose a
prompt-tuning method for clickbait detection via text summarization in this
paper, text summarization is introduced to summarize the contents, and
clickbait detection is performed based on the similarity between the generated
summary and the contents. Specifically, we first introduce a two-stage text
summarization model to produce high-quality news summaries based on pre-trained
language models, and then both the headlines and new generated summaries are
incorporated as the inputs for prompt-tuning. Additionally, a variety of
strategies are conducted to incorporate external knowledge for improving the
performance of clickbait detection. The extensive experiments on well-known
clickbait detection datasets demonstrate that our method achieved
state-of-the-art performance.