Papers › Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks
Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks
Aishwarya Jadhav, Vaibhav Rajan
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Summarization | CNN / Daily Mail (Anonymized) | SWAP-NET | ROUGE-1 | 41.6 | #2 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | SWAP-NET | ROUGE-2 | 18.3 | #2 of 13 | Archive leaderboard | report |
| Text Summarization | CNN / Daily Mail (Anonymized) | SWAP-NET | ROUGE-L | 37.7 | #2 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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