In this paper, we develop a neural multi-document summarization model, named MuD2H (refers to Multi-Document to Headline) to generate an attractive and customized headline from a set of product descriptions. To the best of our knowledge, no one has used a technique for multi-document summarization to generate headlines in the past. Therefore, multi-document headline generation can be considered new problem setting. Our model implements a two-stage architecture, including an extractive stage and an abstractive stage. The extractive stage is a graph-based model that identified salient sentences, whereas the abstractive stage uses existing summaries as soft templates to guild the seq2seq model. A series of experiments are conducted by using KKday dataset. Experimental results show that the proposed method outperforms the others in terms of quantitative and qualitative aspects.