`Keep it Together': Enforcing Cohesion in Extractive Summaries by
  Simulating Human Memory
Ronald Cardenas Matthias Galle Shay B. Cohen
Extractive summaries are usually presented as lists of sentences with no
expected cohesion between them. In this paper, we aim to enforce cohesion
whilst controlling for informativeness and redundancy in summaries, in cases
where the input exhibits high redundancy. The pipeline controls for redundancy
in long inputs as it is consumed, and balances informativeness and cohesion
during sentence selection. Our sentence selector simulates human memory to keep
track of topics --modeled as lexical chains--, enforcing cohesive ties between
noun phrases. Across a variety of domains, our experiments revealed that it is
possible to extract highly cohesive summaries that nevertheless read as
informative to humans as summaries extracted by only accounting for
informativeness or redundancy. The extracted summaries exhibit smooth topic
transitions between sentences as signaled by lexical chains, with chains
spanning adjacent or near-adjacent sentences.