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

POSHAN: Cardinal POS Pattern Guided Attention for News Headline Incongruence