Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Generating indicative-informative summaries with sumUM
Computational Linguistics - Summarization
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A syntactically-based query reformulation technique for information retrieval
Information Processing and Management: an International Journal
An Extractive Text Summarizer Based on Significant Words
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Document Cards: A Top Trumps Visualization for Documents
IEEE Transactions on Visualization and Computer Graphics
GistSumm: a summarization tool based on a new extractive method
PROPOR'03 Proceedings of the 6th international conference on Computational processing of the Portuguese language
Automatic text summarization using two-step sentence extraction
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
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In order to support the processes of reading digital research articles, we propose a summarisation method focused on using the localised topic information of documents. The objective is to produce summaries containing topics which are detailed in a document rather than those representing a general overview. We obtain the summaries by ranking sentences based on key-terms cooccurring throughout the document structure, and key-terms within the syntactic structure of sentences. Our idea of using the concept of syntactic structures is that authors usually have their own writing styles, represented by grammatical structures. In this paper we present a description of the method along with an evaluation, comparing our method to other existing summarisation methods.