Name
Abstract | ||
|---|---|---|
Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understanding of paper content and accurate modeling of cross-paper relationships. Knowledge graphs convey compact and interpretable structured information for documents, which makes them ideal for content modeling and relationship modeling. In this paper, we present |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Volume | Citations | PageRank |
Proceedings of the 29th International Conference on Computational Linguistics | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pancheng Wang | 1 | 0 | 0.68 |
| Shasha Li | 2 | 85 | 20.31 |
| Kunyuan Pang | 3 | 0 | 1.01 |
| Liangliang He | 4 | 2 | 1.06 |
| Dong Li | 5 | 13 | 1.70 |
| Jintao Tang | 6 | 89 | 14.00 |
| Ting Wang | 7 | 36 | 9.43 |