The prior knowledge about the rhetorical structure of scientific abstracts is useful for various text-mining tasks such as information extraction, information retrieval, and automatic summarization. This paper presents a novel approach to categorize sentences in scientific abstracts into four sections, objective, methods, results, and conclusions. METHOD: Formalizing the categorization task as a sequential labeling problem, we employ Conditional Random Fields (CRFs) to annotate section labels into abstract sentences. The training corpus is acquired automatically from Medline abstracts. RESULTS: The proposed method outperformed the previous approaches, achieving 95.5% per-sentence accuracy and 68.8% per-abstract accuracy. CONCLUSION: The experimental results showed that CRFs could model the rhetorical structure of abstracts more suitably.
REF use a 4-way classification scheme for abstracts of scientific papers for identifying objectives, methods, results and conclusions.
Identifying Sections in Scientific Abstracts using Conditional Random Fields
Proceedings of the Third International Joint Conference on Natural Language Processing
Computer Science