  Measuring the congruence between two texts has several useful applications,
such as detecting the prevalent deceptive and misleading news headlines on the
web. Many works have proposed machine learning based solutions such as text
similarity between the headline and body text to detect the incongruence. Text
similarity based methods fail to perform well due to different inherent
challenges such as relative length mismatch between the news headline and its
body content and non-overlapping vocabulary. On the other hand, more recent
works that use headline guided attention to learn a headline derived contextual
representation of the news body also result in convoluting overall
representation due to the news body's lengthiness. This paper proposes a method
that uses inter-mutual attention-based semantic matching between the original
and synthetically generated headlines, which utilizes the difference between
all pairs of word embeddings of words involved. The paper also investigates two
more variations of our method, which use concatenation and dot-products of word
embeddings of the words of original and synthetic headlines. We observe that
the proposed method outperforms prior arts significantly for two publicly
available datasets.
