The task of automatic text summarization produces a concise and fluent text
summary while preserving key information and overall meaning. Recent approaches
to document-level summarization have seen significant improvements in recent
years by using models based on the Transformer architecture. However, the
quadratic memory and time complexities with respect to the sequence length make
them very expensive to use, especially with long sequences, as required by
document-level summarization. Our work addresses the problem of document-level
summarization by studying how efficient Transformer techniques can be used to
improve the automatic summarization of very long texts. In particular, we will
use the arXiv dataset, consisting of several scientific papers and the
corresponding abstracts, as baselines for this work. Then, we propose a novel
retrieval-enhanced approach based on the architecture which reduces the cost of
generating a summary of the entire document by processing smaller chunks. The
results were below the baselines but suggest a more efficient memory a
consumption and truthfulness.

本文研究使用 Transformer 技术来提高自动文本摘要的效率，提出基于检索的方法，可以降低处理整个文档的成本。实验结果表明，相较于基线，该方法的存储占用更少，且保证了文本摘要的可靠性。