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Summarizing text-rich documents has been long studied in the literature, butmost of the existing efforts have been made to summarize a static andpredefined multi-document set. With the rapid development of online platformsfor generating and distributing text-rich documents, there arises an urgentneed for continuously summarizing dynamically evolving multi-document setswhere the composition of documents and sets is changing over time. This isespecially challenging as the summarization should be not only effective inincorporating relevant, novel, and distinctive information from each concurrentmulti-document set, but also efficient in serving online applications. In thiswork, we propose a new summarization problem, Evolving Multi-Document setsstream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSumwith the idea of prototype-driven continuous summarization. PDSum builds alightweight prototype of each multi-document set and exploits it to adapt tonew documents while preserving accumulated knowledge from previous documents.To update new summaries, the most representative sentences for eachmulti-document set are extracted by measuring their similarities to theprototypes. A thorough evaluation with real multi-document sets streamsdemonstrates that PDSum outperforms state-of-the-art unsupervisedmulti-document summarization algorithms in EMDS in terms of relevance, novelty,and distinctiveness and is also robust to various evaluation settings.

PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream