  The abstracts of scientific papers consist of premises and conclusions.
Structured abstracts explicitly highlight the conclusion sentences, whereas
non-structured abstracts may have conclusion sentences at uncertain positions.
This implicit nature of conclusion positions makes the automatic segmentation
of scientific abstracts into premises and conclusions a challenging task. In
this work, we empirically explore using Normalized Mutual Information (NMI) for
abstract segmentation. We consider each abstract as a recurrent cycle of
sentences and place segmentation boundaries by greedily optimizing the NMI
score between premises and conclusions. On non-structured abstracts, our
proposed unsupervised approach GreedyCAS achieves the best performance across
all evaluation metrics; on structured abstracts, GreedyCAS outperforms all
baseline methods measured by $P_k$. The strong correlation of NMI to our
evaluation metrics reveals the effectiveness of NMI for abstract segmentation.
