G reedy CAS : Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information (2023)

Abstract: 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.

## introduction abstracts of scientific papers are short texts that summarize the findings reported in the body text @xcite . a well-formulated abstract forms a scientific inference that extends from premises (e.g., shared knowledge, experimental evidence, or observation) to conclusions (e.g., suggestions, claims, @xcite ). a splitting of an abstract into a conclusion segment and a premise segment can help readers better comprehend how conclusions are drawn @xcite and is of interest for downstream research tasks such as argument generation @xcite , knowledge retrieval @xcite , opinion analysis @xcite , and text summarization @xcite . ## related works abstract segmentation is a particular case of text segmentation. the task of text segmentation is to insert separation markers into the text such that the segmented fragments are topically coherent and comprehensive @xcite . traditional methods can be categorized into supervised and unsupervised methods. unsupervised approaches (alemi and ginsparg, 2015) usually make use of metrics based on textual coherence or topic contiguity and take the following strategies: 1) to greedily seek the best segmentation based on text similarity at each step @xcite ; and 2) to iteratively approach a global optimum of a segmentation objective (e.g. semantic relatedness) via dynamic programming @xcite . these methods use similarity measures or lexical frequencies between segments to determine segmentation boundaries or convert the inter-sentence similarities into a semantic graph and perform graph search @xcite to find the optimal segmentation. supervised methods are usually deployed when sufficiently many annotated examples are available. these methods typically use language models to encode sentences and perform binary classification to predict whether a sentence is on the segmentation boundary @xcite @xcite @xcite . @xcite fine-tuned a hierarchical sentence encoder using structured abstracts to classify sentences into discourse categories (background, technique, and ob-servation). in general, supervised approaches achieve good performance, but they consume a large amount of annotated data, which is expensive to collect. in this work, we set out to test generic unsupervised approaches that require no training data. ## methodology we formulate the task of segmenting scientific abstracts as follows. given an abstract @xmath0, we define as the set of all m a possible segmentations of a. each segmentation g a @xmath1, c a j ) contains a premise segment p a j and a conclusion segment c a j with boundaries given by two indices α a j , ξ a @xmath2:n (range of integers from 1 to n): associated with a corpus @xmath3:k of k abstracts, there is a set of @xmath4:k of k @xmath5. searching for the best ensemble of segmentations of a within g may involve exhaustive enumeration over k @xmath6, which is impossible under limited computational costs. therefore, in this work, we concentrate on greedily approaching a reasonably good segmentation that is a tight lower bound of the actual global optimum. ## conclusion in this explorative work, we propose an unsupervised approach, greedycas, for automatically segmenting scientific abstracts into conclusions and premises. we introduce the cyclic abstract segmentation pipeline, which can be applied to structured and non-structured abstracts. our approach leverages the lexical information between words that co-occur in the conclusion and premise segments and finds the best segmentation of a set of abstracts using nmi as an optimization objective. our empirical results show that nmi is an effective indicator for the segmentation results of scientific abstracts.