Name
Abstract | ||
|---|---|---|
Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.18653/v1/N18-2097 | north american chapter of the association for computational linguistics |
DocType | Volume | Citations |
Journal | abs/1804.05685 | 10 |
PageRank | References | Authors |
0.60 | 15 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arman Cohan | 1 | 139 | 18.25 |
| Franck Dernoncourt | 2 | 149 | 35.39 |
| Doo Soon Kim | 3 | 12 | 2.05 |
| Trung H. Bui | 4 | 86 | 21.88 |
| Seok-Hwan Kim | 5 | 165 | 23.82 |
| Walter Chang | 6 | 251 | 159.67 |
| Nazli Goharian | 7 | 460 | 49.93 |