Name
Abstract | ||
|---|---|---|
This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices. It is a new extractive summarization problem on short text inputs, for which we propose a feature-enriched network model, combining three different categories of features in parallel. Experimental results show that our framework significantly outperforms several baselines by a substantial gain of 4.5%. Moreover, we produce an extractive summarization dataset for E-commerce short texts and will release it to the research community. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1609/aaai.v33i01.33019460 | national conference on artificial intelligence |
Field | DocType | Volume |
Automatic summarization,Information retrieval,Computer science,Mobile device,Artificial intelligence,Machine learning,Network model | Conference | 33 |
Citations | PageRank | References |
1 | 0.48 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Gong | 1 | 132 | 8.35 |
| Xusheng Luo | 2 | 5 | 2.24 |
| Kenny Qili Zhu | 3 | 400 | 39.16 |
| Wenwu Ou | 4 | 191 | 15.56 |
| Zhao Li | 5 | 118 | 29.10 |
| Lu Duan | 6 | 13 | 2.80 |