Extractive summarization systems are known to produce poorly coherent and, if
not accounted for, highly redundant text. In this work, we tackle the problem
of summary redundancy in unsupervised extractive summarization of long,
highly-redundant documents. For this, we leverage a psycholinguistic theory of
human reading comprehension which directly models local coherence and
redundancy. Implementing this theory, our system operates at the proposition
level and exploits properties of human memory representations to rank similarly
content units that are coherent and non-redundant, hence encouraging the
extraction of less redundant final summaries. Because of the impact of the
summary length on automatic measures, we control for it by formulating content
selection as an optimization problem with soft constraints in the budget of
information retrieved. Using summarization of scientific articles as a case
study, extensive experiments demonstrate that the proposed systems extract
consistently less redundant summaries across increasing levels of document
redundancy, whilst maintaining comparable performance (in terms of relevancy
and local coherence) against strong unsupervised baselines according to
automated evaluations.

使用心理语言学理论，我们在提取式摘要中实现了对不重复且连贯的元素进行排名。这个系统可以控制信息检索量的预算，并通过实验证明在处理冗余文档时提供了更少冗余的摘要。