Multi-Document Scientific Summarization from a Knowledge Graph-Centric View
This addresses the challenge of summarizing clusters of scientific papers for researchers, but it is incremental as it builds on existing knowledge graph methods.
The paper tackled the problem of multi-document scientific summarization by proposing KGSum, a model that uses knowledge graphs for encoding and decoding, resulting in substantial improvements over baselines on the Multi-Xscience dataset.
Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understanding of paper content and accurate modeling of cross-paper relationships. Knowledge graphs convey compact and interpretable structured information for documents, which makes them ideal for content modeling and relationship modeling. In this paper, we present KGSum, an MDSS model centred on knowledge graphs during both the encoding and decoding process. Specifically, in the encoding process, two graph-based modules are proposed to incorporate knowledge graph information into paper encoding, while in the decoding process, we propose a two-stage decoder by first generating knowledge graph information of summary in the form of descriptive sentences, followed by generating the final summary. Empirical results show that the proposed architecture brings substantial improvements over baselines on the Multi-Xscience dataset.