Paper: A Discourse-Driven Content Model for Summarising Scientific Articles Evaluated in a Complex Question Answering Task

ACL ID D13-1070
Title A Discourse-Driven Content Model for Summarising Scientific Articles Evaluated in a Complex Question Answering Task
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

We present a method which exploits auto- matically generated scientific discourse an- notations to create a content model for the summarisation of scientific articles. Full pa- pers are first automatically annotated using the CoreSC scheme, which captures 11 content- based concepts such as Hypothesis, Result, Conclusion etc at the sentence level. A content model which follows the sequence of CoreSC categories observed in abstracts is used to pro- vide the skeleton of the summary, making a distinction between dependent and indepen- dent categories. Summary creation is also guided by the distribution of CoreSC cate- gories found in the full articles, in order to adequately represent the article content. Fi- nally, we demonstrate the usefulness of the summaries by evaluating them in a complex...