Leveraging Attribute Conditioning for Abstractive Multi Document Summarization

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 information about a given topic. In this paper we leverage attribute conditioning in order to address the problem of conflicting information in multi document summarization and show strong gains in performance over the base abstractive multi document summarization methods.