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Product reviews summarization is a type of Multi-Document Summarization (MDS)task in which the summarized document sets are often far larger than intraditional MDS (up to tens of thousands of reviews). We highlight thisdifference and coin the term "Massive Multi-Document Summarization" (MMDS) todenote an MDS task that involves hundreds of documents or more. Prior work onproduct reviews summarization considered small samples of the reviews, mainlydue to the difficulty of handling massive document sets. We show thatsummarizing small samples can result in loss of important information andprovide misleading evaluation results. We propose a schema for summarizing amassive set of reviews on top of a standard summarization algorithm. Sincewriting large volumes of reference summaries needed for advanced neural networkmodels is impractical, our solution relies on weak supervision. Finally, wepropose an evaluation scheme that is based on multiple crowdsourced referencesummaries and aims to capture the massive review collection. We show that aninitial implementation of our schema significantly improves over severalbaselines in ROUGE scores, and exhibits strong coherence in a manual linguisticquality assessment.

Massive Multi-Document Summarization of Product Reviews with Weak Supervision