Extractive Summarization with SWAP NET: Sentences and Words from Alternating Pointer Networks
We present a new neural sequence to sequence model for extractive summarization called SWAP NET (Sentences and Words from Alternating Pointer Networks) Extractive summaries comprising a salient subset of input sentences, often also contain important key words. Guided by this principle, we design SWAP NET that models the interaction of key words and salient sentences using a new two level pointer network based architecture. SWAP NET identifies both salient sentences and key words in an input document, and then combines them to form the extractive summary. Experiments on large scale benchmark corpora demonstrate the efficacy of SWAP NET that outperforms state of the art extractive summarizers.
ACL