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- Liakata, Maria, et al.
(author)
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A Discourse-Driven Content Model for Summarising Scientific Articles Evaluated in a Complex Question Answering Task
- 2013
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In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, 18-21 October 2013, Grand Hyatt Seattle, Seattle, Washington, USA. - 9781937284978 ; , s. 747-757
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Conference paper (peer-reviewed)abstract
- We present a method which exploits automatically generated scientific discourse annotations to create a content model for the summarisation of scientific articles. Full papers 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 provide the skeleton of the summary, making a distinction between dependent and independent categories. Summary creation is also guided by the distribution of CoreSC categories found in the full articles, in order to adequately represent the article content. Finally, we demonstrate the usefulness of the summaries by evaluating them in a complex question answering task. Results are very encouraging as ummaries of papers from automatically obtained CoreSCs enable experts to answer 66% of complex content-related questions designed on the basis of paper abstracts. The questions were answered with a precision of 75%, where the upper bound for human summaries (abstracts) was 95%.
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| 2. |
- Liakata, Maria, et al.
(author)
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Automatic recognition of conceptualisation zones in scientific articles and two life science applications
- 2012
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In: Bioinformatics. - : Oxford University Press (OUP). - 1460-2059 .- 1367-4811 .- 1367-4803. ; 28:7, s. 991-1000
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Journal article (peer-reviewed)abstract
- Motivation: Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of eleven categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within a paper and their automatic recognition can greatly facilitate biomedical information extraction by characterising the different types of facts, hypotheses and evidence available in a scientific publication. Results: We have trained and compared machine learning classifiers (SVM and CRF) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognise CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with `Experiment', `Background' and `Model' being the categories with the highest F1-scores (76%, 62% and 53% respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We also discuss the usefulness of automatically generated CoreSCs in two biomedical applications as well as work in progress. Availability: A web-based tool for the automatic annotation of papers with CoreSCs and corresponding documentation is available on-line at http://www.sapientaproject.com/software http://www.sapientaproject.com also contains detailed information pertaining to CoreSC annotation and links to annotation guidelines as well as a corpus of manually annotated papers, which served as our training data. Contact: liakata@ebi.ac.uk
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