  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
https://github.com/suhara/sourcesum.
