  Long documents such as academic articles and business reports have been the
standard format to detail out important issues and complicated subjects that
require extra attention. An automatic summarization system that can effectively
condense long documents into short and concise texts to encapsulate the most
important information would thus be significant in aiding the reader's
comprehension. Recently, with the advent of neural architectures, significant
research efforts have been made to advance automatic text summarization
systems, and numerous studies on the challenges of extending these systems to
the long document domain have emerged. In this survey, we provide a
comprehensive overview of the research on long document summarization and a
systematic evaluation across the three principal components of its research
setting: benchmark datasets, summarization models, and evaluation metrics. For
each component, we organize the literature within the context of long document
summarization and conduct an empirical analysis to broaden the perspective on
current research progress. The empirical analysis includes a study on the
intrinsic characteristics of benchmark datasets, a multi-dimensional analysis
of summarization models, and a review of the summarization evaluation metrics.
Based on the overall findings, we conclude by proposing possible directions for
future exploration in this rapidly growing field.
