Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures

TL;DR

This survey reviews recent advances in knowledge-aware document summarization, focusing on how knowledge and embeddings are integrated into models to improve informativeness, coherence, and factual accuracy.

Contribution

It provides the first systematic taxonomy of knowledge embedding methods in document summarization and analyzes embedding architectures in deep learning models.

Findings

Knowledge-embedded summarizers improve informativeness and coherence.

Various embedding architectures are used in deep learning models.

Challenges include knowledge integration and model explainability.

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.