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.
@article{arxiv.2204.11190,
title = {Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures},
author = {Yutong Qu and Wei Emma Zhang and Jian Yang and Lingfei Wu and Jia Wu},
journal= {arXiv preprint arXiv:2204.11190},
year = {2022}
}