  The anthology of spoken languages today is inundated with textual
information, necessitating the development of automatic summarization models.
In this manuscript, we propose an extractor-paraphraser based abstractive
summarization system that exploits semantic overlap as opposed to its
predecessors that focus more on syntactic information overlap. Our model
outperforms the state-of-the-art baselines in terms of ROUGE, METEOR and word
mover similarity (WMS), establishing the superiority of the proposed system via
extensive ablation experiments. We have also challenged the summarization
capabilities of the state of the art Pointer Generator Network (PGN), and
through thorough experimentation, shown that PGN is more of a paraphraser,
contrary to the prevailing notion of a summarizer; illustrating it's
incapability to accumulate information across multiple sentences.
