Name
Title | ||
|---|---|---|
A Joint Sentence Scoring and Selection Framework for Neural Extractive Document Summarization. |
Abstract | ||
|---|---|---|
Extractive document summarization methods aim to extract important sentences to form a summary. Previous works perform this task by first scoring all sentences in the document then selecting most informative ones; while we propose to jointly learn the two steps with a novel end-to-end neural network framework. Specifically, the sentences in the input document are represented as real-valued vectors... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TASLP.2020.2964427 | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Keywords | Field | DocType |
Bit error rate,Feature extraction,Task analysis,Data mining,Neural networks,History,Training | Automatic summarization,Computer science,Recurrent neural network,Speech recognition,Document summarization,Encoder,Artificial neural network,Sentence | Journal |
Volume | Issue | ISSN |
28 | 1 | 2329-9290 |
Citations | PageRank | References |
3 | 0.39 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingyu Zhou | 1 | 64 | 6.83 |
| Nan Yang | 2 | 583 | 22.70 |
| Furu Wei | 3 | 1956 | 107.57 |
| Shaohan Huang | 4 | 57 | 10.29 |
| Ming Zhou | 5 | 4262 | 251.74 |
| Tiejun Zhao | 6 | 643 | 102.68 |