Leveraging Knowledge-aware Methodologies for Multi-document Summarization

With the development of information technology, a large amount of information and corpora has been incrementally sparked from the Web, stimulating an increasingly high demand for summarizing. Document Summarization is one of Natural Language Processing tasks, which aims to generate abridged versions of a given single or multiple documents as concise and coherent as possible while preserving salient information from the source texts. Recent research in the area has started to use knowledge graphs as they can capture more factual and applicable information from more facets along with source information, benefiting fact consistency and informativeness of generated summaries, rather than just from a linguistic perspective. However, there is no explicit investigation of the effects of different kinds of knowledge graphs on document summarization. The proposed method is to use structured informative and knowledgeable auxiliary information, especially knowledge graphs, into pre-trained summarization models, advancing summary qualities. Expected outcomes are exploring knowledge and knowledge graph incorporation for multi-document summarization, and achieving more informative, coherent, and factually consistent summaries.