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Redundancy-aware extractive summarization systems score the redundancy of thesentences to be included in a summary either jointly with their salienceinformation or separately as an additional sentence scoring step. Previous workshows the efficacy of jointly scoring and selecting sentences with neuralsequence generation models. It is, however, not well-understood if the gain isdue to better encoding techniques or better redundancy reduction approaches.Similarly, the contribution of salience versus diversity components on thecreated summary is not studied well. Building on the state-of-the-art encodingmethods for summarization, we present two adaptive learning models: AREDSUM-SEQthat jointly considers salience and novelty during sentence selection; and atwo-step AREDSUM-CTX that scores salience first, then learns to balancesalience and redundancy, enabling the measurement of the impact of each aspect.Empirical results on CNN/DailyMail and NYT50 datasets show that by modelingdiversity explicitly in a separate step, AREDSUM-CTX achieves significantlybetter performance than AREDSUM-SEQ as well as state-of-the-art extractivesummarization baselines.

AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization