  Summarizing text-rich documents has been long studied in the literature, but
most of the existing efforts have been made to summarize a static and
predefined multi-document set. With the rapid development of online platforms
for generating and distributing text-rich documents, there arises an urgent
need for continuously summarizing dynamically evolving multi-document sets
where the composition of documents and sets is changing over time. This is
especially challenging as the summarization should be not only effective in
incorporating relevant, novel, and distinctive information from each concurrent
multi-document set, but also efficient in serving online applications. In this
work, we propose a new summarization problem, Evolving Multi-Document sets
stream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSum
with the idea of prototype-driven continuous summarization. PDSum builds a
lightweight prototype of each multi-document set and exploits it to adapt to
new documents while preserving accumulated knowledge from previous documents.
To update new summaries, the most representative sentences for each
multi-document set are extracted by measuring their similarities to the
prototypes. A thorough evaluation with real multi-document sets streams
demonstrates that PDSum outperforms state-of-the-art unsupervised
multi-document summarization algorithms in EMDS in terms of relevance, novelty,
and distinctiveness and is also robust to various evaluation settings.
