Conceptual Multi-layer Neural Network Model for Headline Generation

Yi-Di Guo, Heyan Huang, Yang Gao, Chi Lu

Abstract: Neural attention-based models have been widely used recently in headline generation by mapping source document to target headline. However, the traditional neural headline generation models utilize the first sentence of the document as the training input while ignoring the impact of the document concept information on headline generation. In this work, A new neural attention-based model called concept sensitive neural headline model is proposed, which connects the concept information of the document to input text for headline generation and achieves satisfactory results. Besides, we use a multi-layer Bi-LSTM in encoder instead of single layer. Experiments have shown that our model outperforms state-of-the-art systems on DUC-2004 and Gigaword test sets.