Summary++: Summarizing Chinese News Articles with Attention

Abstract

We present Summary++, the model that competed in NLPCC2018’s Summary task. In this paper, we describe in detail of the task, our model, the results and other aspects during our experiments. The task is News article summarization in Chinese, where one sentence is generated per article. We use a neural encoder decoder attention model with pointer generator network, and modify it to focus on words attented to rather than words predicted. Our model archive second place in the task with a score of 0.285. The highlights of our model is that it run at character level, no extra features (e.g. part of speech, dependency structure) were used and very little preprocessing were done.