Knowledge-aware document summarization: a survey of knowledge, embedding methods and architectures

Yutong Qu*, Wei Emma Zhang*, Jian Yang, Lingfei Wu, Jia Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Knowledge-aware methods have boosted a range of natural language processing applications over the last decades. With the gathered momentum, knowledge recently has been pumped into enormous attention in document summarization, one of natural language processing applications. Previous works reported that knowledge-embedded document summarizers excel at generating superior digests, especially in terms of informativeness, coherence, and fact consistency. This paper pursues to present the first systematic survey for the state-of-the-art methodologies that embed knowledge into document summarizers. Particularly, we propose novel taxonomies to recapitulate knowledge and knowledge embeddings under the document summarization view. We further explore how embeddings are generated in embedding learning architectures of document summarization models, especially of deep learning models. At last, we discuss the challenges of this topic and future directions.

Original languageEnglish
Article number109882
Pages (from-to)1-9
Number of pages9
JournalKnowledge-Based Systems
Volume257
DOIs
Publication statusPublished - 5 Dec 2022

Keywords

  • Knowledge
  • Knowledge embedding
  • Document summarization

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