Writing a survey paper on one research topic usually needs to cover the
salient content from numerous related papers, which can be modeled as a
multi-document summarization (MDS) task. Existing MDS datasets usually focus on
producing the structureless summary covering a few input documents. Meanwhile,
previous structured summary generation works focus on summarizing a single
document into a multi-section summary. These existing datasets and methods
cannot meet the requirements of summarizing numerous academic papers into a
structured summary. To deal with the scarcity of available data, we propose
BigSurvey, the first large-scale dataset for generating comprehensive summaries
of numerous academic papers on each topic. We collect target summaries from
more than seven thousand survey papers and utilize their 430 thousand reference
papers' abstracts as input documents. To organize the diverse content from
dozens of input documents and ensure the efficiency of processing long text
sequences, we propose a summarization method named category-based alignment and
sparse transformer (CAST). The experimental results show that our CAST method
outperforms various advanced summarization methods.

提出了一种名为 CAST 的归类对齐和稀疏变换器的自动摘要方法，使用 BigSurvey 数据集，将超过 7000 份调查论文的摘要作为目标摘要，430,000 篇参考论文的摘要作为输入文档，赢得了各种高级自动摘要方法的比较实验。