  Product reviews summarization is a type of Multi-Document Summarization (MDS)
task in which the summarized document sets are often far larger than in
traditional MDS (up to tens of thousands of reviews). We highlight this
difference and coin the term "Massive Multi-Document Summarization" (MMDS) to
denote an MDS task that involves hundreds of documents or more. Prior work on
product reviews summarization considered small samples of the reviews, mainly
due to the difficulty of handling massive document sets. We show that
summarizing small samples can result in loss of important information and
provide misleading evaluation results. We propose a schema for summarizing a
massive set of reviews on top of a standard summarization algorithm. Since
writing large volumes of reference summaries needed for advanced neural network
models is impractical, our solution relies on weak supervision. Finally, we
propose an evaluation scheme that is based on multiple crowdsourced reference
summaries and aims to capture the massive review collection. We show that an
initial implementation of our schema significantly improves over several
baselines in ROUGE scores, and exhibits strong coherence in a manual linguistic
quality assessment.
