SumREN: Summarizing Reported Speech about Events in News


  • Revanth Gangi Reddy University of Illinois at Urbana-Champaign
  • Heba Elfardy Amazon Alexa
  • Hou Pong Chan University of Macau
  • Kevin Small Amazon Alexa
  • Heng Ji Amazon Alexa



SNLP: Summarization, SNLP: Applications, SNLP: Generation, SNLP: Question Answering


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.




How to Cite

Gangi Reddy, R., Elfardy, H., Chan, H. P., Small, K., & Ji, H. (2023). SumREN: Summarizing Reported Speech about Events in News. Proceedings of the AAAI Conference on Artificial Intelligence, 37(11), 12808-12817.



AAAI Technical Track on Speech & Natural Language Processing