Papers › On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
Sandeep Subramanian, Raymond Li, Jonathan Pilault, Christopher Pal
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. We show that this extractive step significantly improves summarization results. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher rouge scores. Note: The abstract above was not written by the authors, it was generated by one of the models presented in this paper.
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Code
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Code Syntology ran Syntology
6 samples harvested; 0 ran; 0 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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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 | Arxiv HEP-TH citation graph | TLM-I+E | ROUGE-1 | 42.43 | #22 of 28 | Archive leaderboard | report |
| Text Summarization | Arxiv HEP-TH citation graph | Sent-PTR | ROUGE-1 | 42.32 | #23 of 28 | Archive leaderboard | report |
| Text Summarization | Arxiv HEP-TH citation graph | Sent-CLF | ROUGE-1 | 34.01 | #27 of 28 | Archive leaderboard | report |
| Text Summarization | Pubmed | Sent-CLF | ROUGE-1 | 45.01 | #19 of 29 | Archive leaderboard | report |
| Text Summarization | Pubmed | Sent-PTR | ROUGE-1 | 43.3 | #23 of 29 | Archive leaderboard | report |
| Text Summarization | Pubmed | TLM-I+E | ROUGE-1 | 41.43 | #25 of 29 | 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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