Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures
This survey organizes existing research for researchers in natural language processing, but it is incremental as it synthesizes rather than introduces new methods.
This paper presents the first systematic survey of knowledge-aware document summarization methods, proposing novel taxonomies to categorize knowledge and embedding techniques while exploring how embeddings are generated in deep learning architectures.
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.