Optimizing convenient online access to bibliographic databases
Information Services and Use
The discourse-level structure of empirical abstracts: an exploratory study
Information Processing and Management: an International Journal
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
An annotation scheme for discourse-level argumentation in research articles
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Extractive summarisation of legal texts
Artificial Intelligence and Law - AI & law in eGovernment and eDemocracy part I
Scientific paper summarization using citation summary networks
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Towards multi-paper summarization reference information
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Generative content models for structural analysis of medical abstracts
LNLBioNLP '06 Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology
Accurate argumentative zoning with maximum entropy models
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Text-level structure of research papers: implications for text-based information processing systems
IRSG'97 Proceedings of the 19th Annual BCS-IRSG conference on Information Retrieval Research
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We present an automated approach to classify sentences of scholarly work with respect to their rhetorical function. While previous work that achieves this task of argumentative zoning requires richly annotated input, our approach is robust to noise and can process raw text. Even in cases where the input has noise (as it is obtained from optical character recognition or text extraction from PDF files), our robust classifier is largely accurate. We perform an in-depth study of our system both with clean and noisy inputs. We also give preliminary results from in situ acceptability testing when the classifier is embedded within a digital library reading environment.