Enhancing Scientific Document Summarization with Research Community Perspective and Background Knowledge

Abstract

Scientific paper summarization has been the focus of much recent research. Unlike previous research which summarizes only the paper in question, or which summarizes the paper and the papers that it references, or which summarizes the paper and the citing sentences from the papers that cite it, this work puts all three of these summarization techniques together. To accomplish this, we have, by utilizing the citation network, introduced a corpus for scientific document summarization that provides information about the document being summarized, the papers referenced by it, as well as the papers that have cited it. The proposed summarizer model utilizes the referenced articles as background information and citing articles to capture the impact of the scientific document on the research community. Another aspect of the proposed model is its ability to generate both the extractive and abstractive summaries in parallel. The parallel training helps the counterparts to improve their individual performance. Results have shown that the summaries are of high quality when considering the standard metrics.