Improving Summarization Quality with Citation Networks in Scientific Documents

The more technology advances, the more scientific articles are published, which places pressure on scientists to absorb information efficiently. Because abstract sections within scientific papers are often influenced by the authors' perspectives, they may not provide a comprehensive view of the document. In this paper, we propose an architecture that generates a comprehensive summary of scientific documents. The main idea is to create summaries that capture the author's perspectives while incorporating insights from the research community. In our architecture, we used both the content of scientific documents and their citation networks as input. Since the citation network holds valuable information curated by humans and mirrors the document's parts of interest to the research community, we used citation sentences to identify significant sentences within the document. Subsequently, we compress the important sentences using a pre-trained summarization model and augment them for the abstract section. Evaluation of our proposed architecture on the CL-SciSumm dataset demonstrates its superior performance. To the best of our knowledge, the proposed architecture outperforms existing models.