Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information

• URL: http://arxiv.org/abs/2305.11553v1
• Date: Fri, 19 May 2023 09:53:45 GMT
• Title: Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information
• Authors: Yingqiang Gao, Jessica Lam, Nianlong Gu, Richard H.R. Hahnloser
• Abstract summary: We empirically explore using Normalized Mutual Information (NMI) for abstract segmentation. On non-structured abstracts, our proposed unsupervised approach GreedyCAS achieves the best performance across all evaluation metrics. The strong correlation of NMI to our evaluation metrics reveals the effectiveness of NMI for abstract segmentation.
• Score: 4.129225533930966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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