Generating a related work section for scientific papers: an optimized approach with adopting problem and method information

The rapid explosion of scientific publications has made related work writing increasingly laborious. In this paper, we propose a fully automated approach to generate related work sections by leveraging a seq2seq neural network. In particular, the main goal of our work is to improve the abstractive generation of related work by introducing problem and method information, which serve as a pivot to connect the previous works in the related work section and has been ignored by the existing studies. More specifically, we employ a title-generation strategy to automatically obtain problem and method information from given references and add the problem and method information as an additional feature to enhance the generation of related work. To verify the effectiveness and feasibility of our approach, we conduct a comparative experiment on publicly available datasets using several common neural summarizers. The experimental results indicate that the introduction of problem and method information contributes to the better generation of related work and our approach substantially outperforms the informed baseline on ROUGE-1 and ROUGE-L. The case study shows that the problem and method information enables considerable topic coherence between the generated related work section and the original paper.