Unsupervised Scientific Abstract Segmentation with Normalized Mutual
  Information
Yingqiang Gao Jessica Lam Nianlong Gu Richard H. R. Hahnloser
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.