Neural abstractive summarization models make summaries in an end-to-end
manner, and little is known about how the source information is actually
converted into summaries. In this paper, we define input sentences that contain
essential information in the generated summary as $\textit{source sentences}$
and study how abstractive summaries are made by analyzing the source sentences.
To this end, we annotate source sentences for reference summaries and system
summaries generated by PEGASUS on document-summary pairs sampled from the
CNN/DailyMail and XSum datasets. We also formulate automatic source sentence
detection and compare multiple methods to establish a strong baseline for the
task. Experimental results show that the perplexity-based method performs well
in highly abstractive settings, while similarity-based methods perform robustly
in relatively extractive settings. Our code and data are available at
this https URL

通过分析源句子，我们研究了神经生成摘要模型是如何将源信息转化为摘要的，包括对参考摘要和 PEGASUS 生成的系统摘要进行源句子注释，并比较了多种自动源句子检测方法以建立强大基线模型。实验结果表明，对于高度抽象的摘要，基于困惑度的方法表现良好，而基于相似度的方法在相对抽取性的摘要中表现稳健。