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A primary objective of news articles is to establish the factual record foran event, frequently achieved by conveying both the details of the specifiedevent (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) andhow people reacted to it (i.e., reported statements). However, existing work onnews summarization almost exclusively focuses on the event details. In thiswork, we propose the novel task of summarizing the reactions of differentspeakers, as expressed by their reported statements, to a given event. To thisend, we create a new multi-document summarization benchmark, SUMREN, comprising745 summaries of reported statements from various public figures obtained from633 news articles discussing 132 events. We propose an automatic silvertraining data generation approach for our task, which helps smaller models likeBART achieve GPT-3 level performance on this task. Finally, we introduce apipeline-based framework for summarizing reported speech, which we empiricallyshow to generate summaries that are more abstractive and factual than baselinequery-focused summarization approaches.

SumREN: Summarizing Reported Speech about Events in News