The abstracts of scientific papers typically contain both premises (e.g., background and observations) and conclusions. Although conclusion sentences are highlighted in structured abstracts, in non-structured abstracts the concluding information is not explicitly marked, which makes the automatic segmentation of conclusions from scientific abstracts a challenging task. In this work, we explore Normalized Mutual Information (NMI) as a means for abstract segmentation. We consider each abstract as a recurrent cycle of sentences and place two segmentation boundaries by greedily optimizing the NMI score between the two segments, assuming that conclusions are strongly semantically linked with preceding premises. 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.

In scientific papers, arguments are essential for explaining authors’ findings. As substrates of the reasoning process, arguments are often decorated with discourse indicators such as “which shows that” or “suggesting that”. However, it remains understudied whether discourse indicators by themselves can be used as an effective marker of the local argument components (LACs) in the body text that support the main claim in the abstract, i.e., the global argument. In this work, we investigate whether discourse indicators reflect the global premise and conclusion. We construct a set of regular expressions for over 100 word- and phrase-level discourse indicators and measure the alignment of LACs extracted by discourse indicators with the global arguments. We find a positive correlation between the alignment of local premises and local conclusions. However, compared to a simple textual intersection baseline, discourse indicators achieve lower ROUGE recall and have limited capability of extracting LACs relevant to the global argument; thus their role in scientific reasoning is less salient as expected.