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Our analysis of large summarization datasets indicates that redundancy is avery serious problem when summarizing long documents. Yet, redundancy reductionhas not been thoroughly investigated in neural summarization. In this work, wesystematically explore and compare different ways to deal with redundancy whensummarizing long documents. Specifically, we organize the existing methods intocategories based on when and how the redundancy is considered. Then, in thecontext of these categories, we propose three additional methods balancingnon-redundancy and importance in a general and flexible way. In a series ofexperiments, we show that our proposed methods achieve the state-of-the-artwith respect to ROUGE scores on two scientific paper datasets, Pubmed andarXiv, while reducing redundancy significantly.

Systematically Exploring Redundancy Reduction in Summarizing Long Documents