  Automatic detection of click-bait and incongruent news headlines is crucial
to maintaining the reliability of the Web and has raised much research
attention. However, most existing methods perform poorly when news headlines
contain contextually important cardinal values, such as a quantity or an
amount. In this work, we focus on this particular case and propose a neural
attention based solution, which uses a novel cardinal Part of Speech (POS) tag
pattern based hierarchical attention network, namely POSHAN, to learn effective
representations of sentences in a news article. In addition, we investigate a
novel cardinal phrase guided attention, which uses word embeddings of the
contextually-important cardinal value and neighbouring words. In the
experiments conducted on two publicly available datasets, we observe that the
proposed methodgives appropriate significance to cardinal values and
outperforms all the baselines. An ablation study of POSHAN shows that the
cardinal POS-tag pattern-based hierarchical attention is very effective for the
cases in which headlines contain cardinal values.
