Exploiting pivot words to classify and summarize discourse facets of scientific papers
The ever increasing number of published scientific articles has prompted the need for automated, data driven approaches to summarizing the content of scientific articles. The Computational Linguistics Scientific Document Summarization Shared Task (CL SciSumm 2019) has recently fostered the study and development of new text mining and machine learning solutions to the summarization problem customized to the academic domain. In CL SciSumm, a Reference Paper (RP) is associated with a set of Citing Papers (CPs) all containing citations to the RP. In each CP, the text spans (i.e. citances) have been identified that pertain to a particular citation to the RP. The task of identifying the spans of text in the RP that most accurately reflect the citance is addressed using supervised approaches. This paper proposes a new, more effective solution to the CL SciSumm discourse facet classification task, which entails identifying for each cited text span what facet of the paper it belongs to from a predefined set of facets. It proposes also to extend the set of traditional CL SciSumm tasks with a new one, namely the discourse facet summarization task. The idea behind is to extract facet specific descriptions of each RP consisting of a fixed length collection of RP's text spans. To tackle both the standard and the new tasks, we propose machine learning supported solutions based on the extraction of a selection of discriminating words, called pivot words Predictive features based on pivot words are shown to be of great importance to rate the pertinence and relevance of a text span to a given facet. The newly proposed facet classification method performs significantly better than the best performing CL SciSumm 2019 participant (i.e. the classification accuracy has increased by 8% whereas regression methods achieved promising results for the newly proposed summarization task.
Scientometrics