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Extractive summarization systems are known to produce poorly coherent and, ifnot accounted for, highly redundant text. In this work, we tackle the problemof summary redundancy in unsupervised extractive summarization of long,highly-redundant documents. For this, we leverage a psycholinguistic theory ofhuman reading comprehension which directly models local coherence andredundancy. Implementing this theory, our system operates at the propositionlevel and exploits properties of human memory representations to rank similarlycontent units that are coherent and non-redundant, hence encouraging theextraction of less redundant final summaries. Because of the impact of thesummary length on automatic measures, we control for it by formulating contentselection as an optimization problem with soft constraints in the budget ofinformation retrieved. Using summarization of scientific articles as a casestudy, extensive experiments demonstrate that the proposed systems extractconsistently less redundant summaries across increasing levels of documentredundancy, whilst maintaining comparable performance (in terms of relevancyand local coherence) against strong unsupervised baselines according toautomated evaluations.

On the Trade-off between Redundancy and Local Coherence in Summarization