SoLSCSum
Minh-Tien Nguyen, Chien-Xuan Tran, Vu Tran, Le-Minh Nguyen · 2016
This paper presents a dataset named SoLSCSum for social context summarization. The dataset includes 157 open-domain articles along with their comments collected from Yahoo News. The articles and their comments were manually annotated by two annotators to extract standard summaries. The inter-annotator agreement is 74.5% and Cohen's Kappa is 0.5845. To illustrate the potential use of our dataset, a learning to rank model was trained by using a set of local and cross features. Experimental results demonstrate that: (1) our model trained by Ranking SVM obtains significant improvements from 5.5% to 14.8% of ROUGE-1 over state-of-the-art baselines in document summarization and (2) our dataset can be used to train summary methods such as SVM.