  Abstractive multi document summarization has evolved as a task through the
basic sequence to sequence approaches to transformer and graph based
techniques. Each of these approaches has primarily focused on the issues of
multi document information synthesis and attention based approaches to extract
salient information. A challenge that arises with multi document summarization
which is not prevalent in single document summarization is the need to
effectively summarize multiple documents that might have conflicting polarity,
sentiment or subjective information about a given topic. In this paper we
propose ACM, attribute conditioned multi document summarization,a model that
incorporates attribute conditioning modules in order to decouple conflicting
information by conditioning for a certain attribute in the output summary. This
approach shows strong gains in ROUGE score over baseline multi document
summarization approaches and shows gains in fluency, informativeness and
reduction in repetitiveness as shown through a human annotation analysis study.
