  A primary objective of news articles is to establish the factual record for
an event, frequently achieved by conveying both the details of the specified
event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and
how people reacted to it (i.e., reported statements). However, existing work on
news summarization almost exclusively focuses on the event details. In this
work, we propose the novel task of summarizing the reactions of different
speakers, as expressed by their reported statements, to a given event. To this
end, we create a new multi-document summarization benchmark, SUMREN, comprising
745 summaries of reported statements from various public figures obtained from
633 news articles discussing 132 events. We propose an automatic silver
training data generation approach for our task, which helps smaller models like
BART achieve GPT-3 level performance on this task. Finally, we introduce a
pipeline-based framework for summarizing reported speech, which we empirically
show to generate summaries that are more abstractive and factual than baseline
query-focused summarization approaches.
