  Some news headlines mislead readers with overrated or false information, and
identifying them in advance will better assist readers in choosing proper news
stories to consume. This research introduces million-scale pairs of news
headline and body text dataset with incongruity label, which can uniquely be
utilized for detecting news stories with misleading headlines. On this dataset,
we develop two neural networks with hierarchical architectures that model a
complex textual representation of news articles and measure the incongruity
between the headline and the body text. We also present a data augmentation
method that dramatically reduces the text input size a model handles by
independently investigating each paragraph of news stories, which further
boosts the performance. Our experiments and qualitative evaluations demonstrate
that the proposed methods outperform existing approaches and efficiently detect
news stories with misleading headlines in the real world.
